Keywords: Production planning and scheduling, Decision-support for human operators, Heuristic and Metaheuristics
Abstract: For processes that rely largely on manual activities, such as order picking, human factors need to be considered for aligning decision-making with actual system performance. This paper proposes a joint order picking batching and sequencing problem formulation with worker fatigue modeling. The fatigue level induces a decrease in performance, thus making the picking time for any batch dependent on prior sequence position decisions. Moreover, overly fatigued operators may necessitate rests. This optimization problem is solved with Adaptive Large Neighborhood Search meta-heuristic for a realistic warehousing scenario. It is shown how considering the fatigue effect can improve both efficiency and worker well-being.

Keywords: Smart transportation, Human-Automation Integration, Facility planning and materials handling
Abstract: Order picking is a critical and labor-intensive warehouse operation, driving significant time and cost demands. Recent advancements have introduced ride-on collaborative robots (cobots) to assist human pickers, improving efficiency. These cobots autonomously follow pickers or serve as a platform for riding during longer distances. In traditional picker-to-parts systems, pickers walk or use manual industrial trucks, stepping on and off frequently. Cobots offer a middle ground, autonomously navigating to the next location while allowing pickers to choose between walking and riding. A key question arises: when should pickers transition between walking and riding to optimize performance time? To answer this, we analyzed real-world warehouse data using a mixed-effects model. Our study evaluated the impact of cobot-assisted traveling on task performance time, considering travel distances and operational dynamics. Results reveal a critical threshold of 6.4 meters, beyond which riding becomes more efficient than walking. These findings offer actionable insights for optimizing human-robot collaboration in order picking systems. Training programs can emphasize this threshold to maximize productivity, enabling dynamic adjustments during operations. The research is especially valuable for warehouses employing picker-to-parts systems with cobots, providing a data-driven approach to enhance efficiency and reduce operational costs.

Keywords: Simulation technologies, Discrete event systems in manufacturing, Facility planning and materials handling
Abstract: The rise of highly customized manufacturing setups has challenged the current intralogistics systems, requiring greater flexibility in material handling. This paper proposes integrating drones as a complement to existing indoor material handling technologies. Unlike existing systems, the proposed framework leverages drones to enhance adaptability and responsiveness in dynamic production environments. A discrete event simulation of the drone delivery system is developed using FlexSim and compared with traditional batch production processes. The results showcase the potential of drone-based intralogistics to address emerging demands in flexible manufacturing setups, providing a new paradigm for material transport in highly variable industrial settings.

Keywords: Scheduling, Sustainable Manufacturing, Supply Chain Management
Abstract: The issue of resource scarcity on our planet is a significant challenge that can be addressed through the implementation of circular economy principles. In the context of manufacturing, this entails the recovery of resources at the conclusion of a product’s life cycle, with the objective of facilitating their reuse in future applications. In the resource recovery process by Navtn-Chandra (1994), recycling is the most widely known method of resource recovery. However, a significant amount of energy is expended in the process of reducing products to their constituent materials and returning them to their original state. In the context of resource recovery, recycling should be regarded as a last resort for the recovery of materials from end-of-life products. This is where remanufacturing is set to assume a pivotal role in the future. Remanufacturing is a process whereby as much of the product as possible is reused at the end of its life cycle, with a view to minimizing energy consumption in the production process. In a factory, end of life products that are used as resources are therefore subjected to a process of analysis in order to identify which components can be reused, require reprocessing, or must be discarded and remade. The input of highly uncertain resources in which all failures can occur demand a versatile manufacturing plant. The processes that happen in the factory are as unique as the input resources and are therefore seen as unique processes (Pawlik et al. (2013)). This means that all systems in an automated remanufacturing plant, including the intralogistics system, need to be able to handle short decision horizons and uncertainty. This work presents an outline on how a modular and decentralized multi-robot task allocation algorithm can contribute to the control of AMRs in a circular factory.

Keywords: Production planning and scheduling, Scheduling, Operations Research
Abstract: In manufacturing, buffers improve machine utilization by reducing starvation and blocking caused by variable job processing times. Moreover, companies also use shared buffers to further reduce costs, especially when space is limited and fluctuating workloads, common in customized production, cause different machines to become bottlenecks at different times. Unlike local buffers, shared buffers serve multiple machines. Shared buffers can be either stationary or mobile. Yet, in machine scheduling, buffer space is often neglected, or only local buffers are considered. Against this background, the aim of this project is to design algorithms for integrated material handling and machine scheduling with shared buffers. In this paper, we present a comprehensive model that exploits state-of-the-art modeling techniques from scheduling and generalized vehicle routing. This model is conceptualized as a starting point for a customized decomposition-based solution approach.

Keywords: Scheduling, Distributed systems and multi-agents technologies, Heuristic and Metaheuristics
Abstract: Implementing complex online or offline scheduling solutions for machine shop scheduling is often difficult in practice, as these methods can be computationally expensive and difficult to understand. To provide a transition to these more complex solutions, this paper compares our proposed scheduling method based on a multi-agent system with state-of-the-art traditional offline and reinforcement learning-based online methods. Computational results indicate that our method is comparable to the existing complex offline and online methods, while computationally more advantageous, showcasing its effectiveness and flexibility.

Keywords: Optimisation Methods and Simulation Tools, Operations Research, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: Poor maintenance practices in fiberglass manufacturing cause downtimes, quality defects, and increased costs. This study proposes a predictive maintenance (PdM) optimization framework combining a data-driven system, employing a convolutional neural network (CNN) model to predict bushing operational efficiency (OE), and a model-based system that operates a multi-objective optimization approach for scheduling maintenance. The data-driven system identifies critical bushings by predicting OE zones, while the model-based system prioritizes tasks based on criticality levels, minimizing maintenance completion time and servicing costs hierarchically. Results demonstrate criticality-based scheduling where higher-priority bushings are serviced earlier, while lower-priority bushings face longer waiting times. Operator utilization is optimized with balanced task allocation and sequential execution, ensuring efficient resource use and minimized downtime. The integrated framework improves operational efficiency, reduces delays, and addresses urgent tasks, which offers a robust solution for predictive maintenance in fiberglass production.

Keywords: Supply Chain Management, Risk Management, Supply chains and networks
Abstract: The automotive industry is facing increasing challenges due to shifting market demands and technological changes. These developments put pressure on suppliers to ensure consistent supply security and make the supply chain more vulnerable to operational risks. This paper proposes a data-driven framework to assess these risks by leveraging data exchange between an OEM and its suppliers to improve the early detection of emerging risks. Using key performance indicators derived from operational data, the proposed framework identifies, analyzes, and predicts the impact of risks on supply capabilities, supporting proactive risk management. Pattern recognition and simulation enhance early warning capabilities, while expert-based evaluation supports decision-making for supply chain resilience. This framework fills a gap in predictive supply chain risk assessment and emphasizes industry-specific data insights.

Keywords: Sustainable Manufacturing, Modeling, simulation, control and monitoring of manufacturing processes, Decision Support System
Abstract: The increasing focus on environmental sustainability in industry underscores the importance of accurately predicting energy consumption to achieve efficiency goals. In this context, distributed inference proves to be a promising approach, utilizing the extensive data produced by distributed sensors in industrial environments. This study aims to create a novel methodology for precise energy consumption forecasting by integrating centralized training with distributed inference. The proposed solution has been tested on a real pilot case, a production line provided by a manufacturing company, and the results demonstrate the effectiveness of distributed inference frameworks in promoting industrial sustainability.

Keywords: Optimisation Methods and Simulation Tools, Decision-support for human operators, Scheduling

Keywords: Production planning and scheduling, Heuristic and Metaheuristics, Industrial and applied mathematics for production
Abstract: This paper deals with the assignment of parts to jobs (nesting) in Additive Manufacturing (AM) and the scheduling of jobs in non-identical 3D printing machines. The integrated approach of nesting and scheduling is focus on minimizing three alternative objectives: production costs, total delays and makespan. The problem considers the possibility of incorporating different orientations of parts and 2D rectangular bin packing techniques to be grouped in jobs, increasing the complexity and approaching a real situation in the industry. This extra guidance component allows to reduce total production costs, part delays and makespan, especially in highly complex problems. These problems are solved using a semiparallel construction heuristic framework. This kind of heuristics are currently of great scientific interest but have not been evaluated in this way until now in additive manufacturing problems allowing alternative orientations and 2D nesting. In order to validate the performance of the algorithm, computational experiments were carried out on benchmark instances with one orientation in parts. New sets of benchmark cases adapted from the literature with some alternative orientations in parts are also presented and solved to minimize the three objectives separately. It is observed that the proposed method has a good performance and provides reasonably costs- time- and delay- savings when alternative part orientations are used.

Keywords: Supply Chain Management
Abstract: Additive Manufacturing (AM), also known as rapid prototyping or 3D printing, is widely used across various industries, including medical products and automotive spare parts. The COVID-19 pandemic has further accelerated its adoption to address supply chain disruptions caused by shortages in production resources and logistics constraints. However, as AM integrates into supply chains, structural changes in nodes and data flows create new challenges in information sharing and data standardization. Ontologies have proven effective in enhancing data interoperability and improving information quality through semantic modeling. Despite this, a comprehensive approach that combines AM and logistics ontologies to address cross-domain challenges remains underexplored. This study develops an ontology-based supply chain model for AM by integrating existing AM and logistics ontologies. Using the Design Science Research Methodology (DSRM), the proposed ontology is constructed and instantiated with a sample dataset for validation. The results provide a foundational framework for improving data management and coordination in AM supply chains.

Keywords: Smart manufacturing systems, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: Process monitoring is crucial for ensuring both part quality and process stability in Wire Arc Additive Manufacturing (WAAM). Arc electrical signals have been shown to be valuable indicators of process dynamics, as they reflect variations in stability during deposition. This study explores the application of signal processing and machine learning techniques to analyze these arc signals, aiming to extract relevant features for detecting instability. A machine learning classifier is used to distinguish between stable and unstable process states. The results demonstrate the potential of these monitoring criteria for instability detection, offering a tool for quality assurance and improved performance in WAAM. Finally, a case study is presented to show the practical application of the model in detecting local instability within a deposition layer.

Keywords: Supply chains and networks, Supply Chain Management, Industry 4.0
Abstract: Metal Additive Manufacturing (MAM) is transitioning from a prototyping technique to broader industrial adoption, creating new opportunities for the steel supply chain. Integrating Directed Energy Deposition (DED) into steelmaking can enable near-net shape component formation, localized production, and flexible repair strategies. These developments can streamline material flows, reduce inventory levels, and enhance responsiveness to shifting market demands. However, realizing these benefits requires confronting challenges related to specialized feedstock procurement, quality control, and secure digital asset management. Thus, this paper investigates how MAM can affect sourcing, production, and delivery in the steel industry. By examining the upstream implications of DED integration, this work outlines prerequisites for successful adoption and offers insights into how MAM can foster more efficient, agile, and sustainable steel supply chains.

Keywords: Knowledge management in production, Supply Chain Management, Operations Research
Abstract: In the modern era, where technology is integral to our lives and drives collaborative information sharing, concerns over data ownership remain prevalent. This issue is particularly evident in additive manufacturing, where collaborative projects often involve multiple supply chain actors. These collaborations introduce unique challenges, especially in intellectual property management and contractual agreements. This paper explores the risks of handling intellectual property in these collaborative environments, focusing on the complex interactions between supply chain actors that could compromise intellectual property security in additive manufacturing applications. Through a survey of key stakeholders, the paper presents the views of experts that aid in identifying which additive manufacturing process streams and contractual intellectual property assets are most vulnerable to risks of being compromised when dealing with specific supply chain actors. The findings emphasise the importance of establishing robust contractual structures, clear information and parts exchange protocols, and well-defined intellectual property management practices to ensure successful, risk-minimised collaborations in additive manufacturing projects. Also, these results are anticipated to stimulate interest that informs further research and practice adjustments to advance co-creation, distributed manufacturing, and sustainable business models.

Keywords: Human-Automation Integration, Robotics in manufacturing, Modeling, simulation, control and monitoring of manufacturing processes

Keywords: Human-Automation Integration, Smart manufacturing systems, Decision-support for human operators
Abstract: This paper introduces a trust-based human-machine reciprocal learning (HMRL) framework to foster inclusive manufacturing by providing integrated cognitive support for neurodiverse workforces in human-robot collaborative (HRC) environments. The framework's architecture comprises five key modules: cognitive assessment, real-time object detection, natural language processing, a multi-modal instruction interface, and collaborative robotics. A reciprocal learning mechanism facilitates continuous improvement in both the machine learning models and the personalized assistance provided to workers with cognitive differences. Evaluation across diverse manufacturing scenarios demonstrates significant gains in assembly performance, including improved quality and reduced cycle time. This HMRL framework lays the groundwork for symbiotic human-robot manufacturing.

Keywords: Human-Automation Integration, Robotics in manufacturing, Industry 4.0
Abstract: To fully leverage the capabilities of both the robot and the human operator while working towards shared goals, effective communication and interaction between them are crucial. However, in many collaborative robot applications, interaction is often restricted, resulting in ineffective and unintuitive collaboration. Speech is a promising communication method for human-robot collaboration, as it is natural for the human operator and allows for two-way communication. In this work, we demonstrate a collaborative robotic system that integrates speech recognition, text-to-speech, object detection, and robot control. Object detection equips the robot with environmental awareness, while the speech-related functionalities enable bidirectional communication. The system is tested through several assembly-related test cases, demonstrating basic and advanced communication features such as task coordination, dialogue, and quality inspection.

Keywords: Smart manufacturing systems, Human-Automation Integration, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: Operator intention recognition has been extensively studied in human-robot collaboration (HRC). However, most existing methods struggle to accurately analyze task-related contexts for similar tasks in personalized product assembly, fail to focus on the most relevant assembly areas, and lack dynamic guidance to intention recognition results, thus compromising their stability. This paper proposes a large language model (LLM)-powered operator intention recognition method. First, the analytical and reasoning capabilities of LLM are utilized to analyze assembly tasks, providing localization of assembly areas and guidance information for intention recognition. Furthermore, to address the uncertainty of operator behavior, a dynamic collaborative information-enabled intent recognition method is designed. This approach extracts the semantic meanings of object manipulation to strengthen the intrinsic correlation between human actions and assembly tasks, suppressing irrelevant information interference and achieving accurate operator intention recognition. By integrating dynamic collaborative information and human actions, it achieves accurate operator intent recognition. Finally, an HRC assembly case is used to demonstrate the practicality of the proposed method.

Keywords: Quality management, Design and reconfiguration of manufacturing systems, Decision-support for human operators
Abstract: This paper presents a novel approach to the execution of manufacturing processes, based on an inclusive strategy for developing work instructions tailored to the diverse needs of employees. The conventional approach to instructions frequently proves inadequate in accommodating the various modes of information processing, resulting in diminished efficiency and an elevated incidence of errors in production. The study examines the implementation of multimodal instructions that all together integrate visual, textual, auditory, and kinaesthetic elements, as well as the modes of presenting task sequences for execution. We argue that this approach not only helps neurodiverse employees in understanding and applying the instructions, but also benefits all workers by improving overall comprehension of the process, reducing execution time, and cutting the number of mistakes and non-conformities. Empirical data from studies conducted in a simulated manufacturing environment substantiate the efficacy of this approach in optimizing the production process and fostering a more efficient and inclusive work environment.

Keywords: Production planning and scheduling, Supply Chain Management
Abstract: The accurate forecasting of demand is a major challenge for production companies, especially for companies that engage in make-to-order production. Accurately anticipating demand enables companies to develop a robust production program and mitigate overloading or underutilization of production resources. Light Gradient Boosting Machine (LightGBM) is a Machine Learning (ML) algorithm, developed by Microsoft Research, capable of performing regression, classification, and ranking tasks. The algorithm gained attention in recent years due to its ability to efficiently process large datasets and a high number of features while still being cheap in terms of computational costs. However, the quality of the results largely depends on setting the right parameters carefully. Hyperparameter optimization, such as GridSearch, can be used to find suitable parameters, but these methods are very time-consuming and require users to limit both the number of parameters and the range of the respective values. This paper aims to research the impact of the parameters of LightGBM on the predictive performance of regression tasks. To achieve this task, a total of 2,592 simulated sales datasets were created, each varying in seasonality, seasonal duration, seasonal amplitude, linear growth and random noise. For each of the datasets a LightGBM model was trained, using hyperparameter optimization. The models were then compared using the Root Mean Squared Error (RMSE) as a metric to find the best performing models. A thorough analysis of these parameters provides insight into the importance of different parameters for regression tasks and can be utilized to speed up hyperparameter optimization of future regression LightGBM-based regression models.

Keywords: Industry 4.0, Knowledge management in production, Decision Support System
Abstract: The Industry 4.0 revolution has introduced a new era in manufacturing where Artificial Intelligence (AI) plays a pivotal role. However, small and medium-sized enterprises (SMEs) face challenges in selecting and implementing appropriate AI tools. This paper examines the decision-making process to align strategic objectives with the best choice of AI tools for specific needs. The core outcome is a conceptual framework defining the solution space where AI enhances shop floor operations. Based on a literature analysis and the authors’ expertise, the framework formalizes objectives and identifies critical challenges AI can solve, offering practitioners practical guidance to align with organizational goals. The framework has been tested with a pilot of the KITT4SME project, leading to the selection of an AI-based ergonomic monitoring solution that improved both productivity and worker well-being.

Keywords: Design and reconfiguration of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes, Facility planning and materials handling
Abstract: In today’s industrial landscape, Digital Twins and Virtual Engineering are revolutionizing production system planning by enabling accurate simulations and reducing the need for physical prototypes. However, these advancements depend on effective buyer-supplier collaboration, where trust, dependency, and transparency play critical roles. Trust ensures confidence in simulation accuracy and supplier capabilities, while dependency arises from the reliance on supplier-provided tools and expertise. Transparency bridges the gap by enabling buyers to evaluate models effectively and make informed decisions. This study examines the interplay of these three dimensions, highlighting their relevance in managing buyer-supplier dynamics and providing strategies to enhance collaboration through insights from a systematic literature review.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Scheduling, Robotics in manufacturing
Abstract: With the transformation towards circular value chains, product disassembly as well as the digitization and automation of this process are of growing importance. The research project iDeaR is focusing the development of novel methods and technologies for the digitized and automated product disassembly. Part of these is the development of a digital representation of disassembly sequences which is required for automated dismantling processes and which also shall be a proposal, how disassembly sequences can be structured as part of digital product passports. This paper is describing the developed approach as also its first technical implementation and integration with sub-processes of an automated disassembly process.

Copyright © 2025 IFAC

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Risk Management, Discrete event systems in manufacturing
Abstract: Companies with consistently positive cash cycles tend to outperform those with negative cycles, which struggle to cover operational and capital expenses. This study focuses on a Mexican company facing financial liquidity challenges due to a high investment in finished product inventory and an 80-day cash conversion cycle, with 73 days tied to inventory. Using discrete event simulation and digital modeling, the production process of fluid extract was analyzed to identify inefficiencies and propose improvements. The simulation, conducted with Anylogic software, revealed significant delays in the percolation operation, contributing to a 20-day production process and substantial work-in-process inventory. This work lays the foundation for implementing a digital twin to evaluate real-time financial impacts of operational changes, ultimately improving the company’s financial performance.

Keywords: Transportation Systems, Supply chains and networks, Operations Research
Abstract: This research presents an advanced approach to optimize electric vehicle (EV) routing in logistics networks. The study develops a mathematical model that incorporates different types of EVs, including trucks and motorcycles, each with distinct capacities, energy consumption rates, and service costs. The model includes energy and load transfer at joint nodes for vehicles to collaborate by sharing energy and redistributing loads, which extends their operational ranges and optimizes route feasibility. Key constraints ensure realistic energy usage, service times, and vehicle capacity considerations, while the objective function minimizes total system costs, including fixed, travel, energy, and service costs. Results show that adjusting systems capacity and service times are two crucial factors that impact the cost and feasibility of the routing network. This work contributes to developing practical, adaptable, and eco-friendly routing solutions for modern electric vehicle logistics networks.

Keywords: Simulation technologies, Design and reconfiguration of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Due to large-scale structural reforms, the production of semi-finished products that were manufactured and used within the steelworks was halted, and these products are now transported from other factories. Since logistics between and within factories will change significantly, we conducted a discrete-event simulation-based verification of transport capacity before implementing the reforms. We evaluated the resilience to delays caused by severe weather conditions by varying the transport frequency and introducing the occurrence of severe weather based on actual probability distributions. Ultimately, we confirmed that the cargo handling capacity is sufficient to recover even from the most severe weather damage.

Keywords: Supply Chain Management, Scheduling, Operations Research
Abstract: This work introduces a new MILP mathematical model for the poultry supply chain, which simultaneously addresses two main tasks: (1) the assignment and scheduling of the vehicles devoted to transport poultry from breeding farms to the slaughterhouse, and (2) the scheduling of the operations at the slaughterhouse. A set of reduced size instances is solved with CPLEX to illustrate the results obtained, although the model complexity prevents from obtaining feasible solutions in some cases.

Keywords: Optimization and Control, Transportation Systems, Decision-support for human operators
Abstract: This study examines the optimization of fleet utilization at Stavanger University Hospital (SUS), Norway, to enhance the sustainability of its employee transportation system. A mixed-method approach, integrating both quantitative and qualitative methods, was employed to achieve this objective. Data were collected through fleet tracking system analysis, surveys, interviews, and a review of existing literature. The findings highlight suboptimal efficiency rates, high idle times, and underutilized vehicles within the SUS fleet. To assess a sustainable and optimal car-sharing system, the study applied the TOPSIS framework, which indicated that the existing car-sharing system best aligns with SUS’s requirements. This research contributes to the field of logistics and transportation by offering insights for organizations seeking to improve fleet efficiency, support data-driven decision-making, and promote sustainable practices.

Keywords: Production planning and scheduling, Scheduling, Industry 4.0
Abstract: This study presents a review of Reinforcement Learning (RL) as a tool for enhancing the efficiency and flexibility of Manufacturing Production Systems (PS) related to dynamic, Flexible Job Shop Scheduling Problems (DFJSSP) and Flexible Job Shop Scheduling Problems (FJSSP). Specifically, it identifies gaps and emerging trends. Although RL has shown significant advantages over traditional methods, the findings reveal critical gaps, including a lack of comparative studies between RL variants, its application in large-scale systems, and limited validation in the real world. This study emphasizes RL's role in Industry 4.0, providing a roadmap for implementing adaptive, sustainable, and intelligent PS.

Keywords: Supply Chain Management, Sustainable Manufacturing, Optimization and Control
Abstract: This study offers a comparative analysis of digital initiatives in Purchasing and Supply Management (PSM), utilizing the Delphi technique and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE II). Diverse digital initiatives were assessed and ranked in accordance with a comprehensive examination of critical factors such as cost, risk, delivery capability, environmental impact, and technological integration. The results indicate that the integration of cross-functional collaboration with procurement digitalization platforms is a key initiative. This highlights the importance of collaborative efforts and digital solutions for achieving successful transformation. The study provides valuable insights into digitalization initiatives and contributes to expanding the understanding of how digital technologies can be leveraged in purchasing and supply management. These findings offer actionable recommendations for decision-makers in the manufacturing sector who aim to enhance their operations through digitalization, fostering greater efficiency, transparency, and competitiveness in their procurement processes.

Keywords: Knowledge management in production, Business Process Modeling, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The transition to a circular economy requires models that capture the complex interdependencies of recycling and resource reuse across industries, which traditional linear models fail to capture. This study extends the Product-Process-Resource (PPR) model by integrating circular flows, feedback loops and cross-company collaboration features. A case study on multi-metal recycling of Waste Electrical and Electronic Equipment (WEEE) demonstrates its application in integrating material flows and fostering stakeholder co-ordination. The extended PPR model serves as a conceptual foundation, enabling sustainable practices and supporting the development of detailed MBSE models tailored for circular production systems.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Sustainable Manufacturing, Decision Support System
Abstract: This work presents a modular framework that integrates digital twins with intelligent agents for increasing energy efficiency and reducing emissions of manufacturing systems. The presented framework includes real-time monitoring with dynamic thresholding for enhancing sustainability, scalability, and operational effectiveness. Demonstrated through synthetic data simulations, the approach shows measurable improvements in energy consumption and CO2 emissions, justified by two main methods: variable monitoring and adaptive parameter control. The results underline the important role of intelligent agents and digital twins in enabling sustainable and circular manufacturing technologies, according to the principles of Industry 4.0 and strategies for future-ready production.

Keywords: Industry 4.0, Sustainable Manufacturing, Smart manufacturing systems
Abstract: This paper brings a reflection on the role of the Cognitive Digital Twin as a novel artifact to contribute to the deployment of Circular Manufacturing strategies. The paper aims to provide an initial discussion with the purpose of contributing to the development of a research vision towards the habilitation of Circular Manufacturing strategies through the support of the adaptive cognitive-based responsiveness promised by the Cognitive Digital Twin technology. Specifically, the paper proposes a framework to identify the industrial problems that can be addressed and to envision the role of Cognitive Digital Twins with respect to the deployment of Circular Manufacturing strategies. The automotive manufacturing industry is adopted to set the scene where the role of Cognitive Digital Twins for Circular Manufacturing strategies is discussed, also through the illustration of a few exemplary cases as possible applications.

Keywords: Industry 4.0, Simulation technologies, Optimisation Methods and Simulation Tools
Abstract: Digital twins (DTs) are representations of real-world objects conceived to enhance the management of tangible goods. They mirror physical entities across various domains offering services such as description, diagnosis, monitoring, forecasting, and control. The rapid growth of DTs has attracted interest from stakeholders in circular economies (CEs). CEs are networks where different actors manage materials, and also information, in a non-linear manner. The primary goal of CEs is to minimize waste of resources and extraction of materials along the product chain. DTs are considered as the key digital enabler for reaching high efficiency throughout the value chain of CEs. This conceptual paper analyses the essential features characterizing CEs that need to be represented in the DT, including resource efficiency, longevity of products, circularity, and stochasticity. Each feature is analysed in terms of challenges companies face with developing and maintaining DTs for CEs.

Keywords: Design and reconfiguration of manufacturing systems, Quality management, Industry 4.0
Abstract: The increasing variability and complexity of small-batch production pose significant challenges for ensuring assembly quality. To address these challenges, an Assembly Quality Inspection Framework integrating Human-Cyber-Physical Systems (HCPS) and Hierarchical Asset Administration Shells (HAAS) is proposed to enhance adaptability and efficiency in small-batch manufacturing environments. Specifically, the framework consists of design, manufacturing, assembly, and inspection spaces. HCPS enables dynamic collaboration and decision-making, while HAAS provides a standardized digital interface for interoperable and scalable system integration. Finally, a Coarse Pointing Mechanism (CPM) assembly quality inspection platform is designed and implemented, demonstrating the effectiveness of the proposed framework.

Keywords: Production planning and scheduling, Smart manufacturing systems, Decision Support System
Abstract: In recent years, laser surface processing has become an essential technique for improving the performance of steel materials. The precision and quality of laser processing outcomes are heavily influenced by various processing parameters, such as pulse mode, pulse frequency, defocusing amount, marking frequency, and pulse energy. However, traditional research methods, such as trial-and-error and experimental design, are inefficient and limited in capturing the complex relationships between laser parameters and processing outcomes. Therefore, this study presents a data-driven framework for predicting and analyzing laser processing outcomes. We conducted a series of laser surface texturing experiments to collect data on laser parameters and their corresponding outcomes. The dataset includes random combinations of pulse energy, defocusing amount, pulse frequency, pulse mode, and marking frequency. These experiments served as the foundation for model training and correlation analysis. Using Spearman’s rank correlation coefficient and maximal information coefficient, we analyze the impact of input parameters on processing results. By comparing multiple machine-learning models, including polynomial regression, support vector regression, random forest, and multi-layer perception, we evaluate their performance in predicting processing outcomes. The results demonstrate that the data-driven framework effectively identifies key correlations between laser parameters and processing outcomes, and the comparison of machine learning models reveals that random forest performs best in predicting depth, support vector regression excels in predicting surface roughness, and multi-layer perception provides the most accurate predictions for diameter, offering valuable insights for selecting optimal laser parameters in industrial applications.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Quality management, Knowledge management in production
Abstract: Fault diagnosis is crucial for ensuring the reliability of manufacturing systems. However, a large proportion of unlabeled data remains unused, which presents a challenge that limits fault diagnosis performance. While some semi-supervised methods focus on leveraging unlabeled data, they rely solely on statistical properties and lack domain knowledge, leading to performance bottlenecks. In this paper, we propose a fault diagnosis framework that leverages Large Language Models (LLMs), which contain vast amounts of prior knowledge, to enhance classification performance. We train a base classifier on labeled data, generate pseudo-labels for unlabeled data, and fine-tune LLaMA3 to infer another set of pseudo-labels. By merging these labels and adjusting sample weights, we create an augmented training set, which improves model performance. Our approach outperforms traditional methods as demonstrated on the Tennessee Eastman Process dataset.

Keywords: Operations Research, Smart transportation, Optimization and Control
Abstract: In response to the growing demand for low-carbon buildings and industrialized construction, Autoclaved Lightweight Concrete (ALC) panels are gaining importance due to their durability and porosity. For the mold assembly process in ALC panel production—which plays a crucial role in maintaining overall production efficiency and minimizing material waste—algorithmic solutions are needed to optimize mold usage. This study introduces two innovations to address the order-driven mold assembly problem in ALC panel production. First, the problem is divided into two stages: the first involves the allocation of orders to production lines, and the second focuses on mold assembly within a single production line. These two stages interact iteratively to refine the solution. Second, a nested column generation framework is employed in the second stage, iteratively optimizing mold usage by addressing suboptimal configurations. Numerical experiments confirm that the proposed algorithm enhances the precision and efficiency of the mold assembly process, effectively reducing mold usage, production costs, and material waste, thus mitigating environmental impact.

Keywords: Scheduling, Smart manufacturing systems
Abstract: Scheduling represents a fundamental requirement under numerous scenarios in various systems. However, the solution progress of scheduling often imposes limitations on the flexibility of these systems. In this paper, the data-virtual-physical spaces of systems are integrated and fused into a flexible framework for scheduling inspired by Digital Twin. The framework tracks the scheduling state in real-time by utilizing Time Petri Nets and makes decisions of scheduling actions assisted by Deep Reinforcement Learning agents trained according to scheduling tasks. Furthermore, this paper details the specific implementation methods for constructing this framework through an experiment on a wafer sorting system as a case in point. The experimental results show that the framework is capable of attaining efficient and flexible scheduling performance on different scheduling tasks.

Keywords: Fuzzy logic control, Modeling, simulation, control and monitoring of manufacturing processes, Risk Management
Abstract: Accurate and efficient forecasting of multivariate time series data is crucial in industrial and logistics applications, where predictive accuracy and computational efficiency drive operational performance. This paper presents the Bi-level Self-Organizing Maps-Weighted Multivariate Fuzzy Time Series (BSOM-WMVFTS), a multivariate forecasting model, and evaluates its performance against the Self-Organizing Map-Fuzzy Time Series (SOM-FTS) model, introduced by dos Santos et al. (2021), in an industrial forecasting context. Application scenarios further demonstrate the practical value of the proposed model in industrial and logistics operations. The model is evaluated using a real-world industrial dataset, where the liquid level of a selected tank is predicted based on multiple exogenous variables. Forecasting performance is assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), while model complexity is measured by the number of fuzzy rules. Results show that BSOM-WMVFTS outperforms the SOM-FTS model in MAE and RMSE, demonstrating improved accuracy and better error management. Additionally, BSOM-WMVFTS requires fewer fuzzy rules, making it a more efficient and scalable solution for real-time forecasting applications. These findings underscore the potential of BSOM-WMVFTS in improving forecasting reliability and optimizing operational efficiency across industrial and logistics applications.

Keywords: Smart manufacturing systems, Risk Management, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: Component obsolescence poses significant challenges in industries reliant on electronic components, causing increased costs and supply chain disruptions. Accurate obsolescence risk prediction is essential but hindered by a lack of reliable data. This paper proposes a novel approach to forecasting obsolescence risk using zero-shot learning (ZSL) with large language models (LLMs) to address data limitations by leveraging domain-specific knowledge from tabular datasets. Applied to two real-world datasets, the method demonstrates effective risk prediction. A comparative evaluation of four LLMs underscores the importance of selecting the right model for specific forecasting tasks.

Keywords: Decision-support for human operators, Monitoring, diagnosis and maintenance of manufacturing systems, Industry 4.0
Abstract: Small deviations in the production cycle can cause expensive downtime or quality deviations in high-volume, high-precision production lines. If no precise root cause can be identified, only the symptoms are eliminated, resulting in a pattern of repetitive failures and temporary remedial measures. This paper presents a knowledge-based framework and algorithm that combines network science and graph theory to detect anomalies and identify root causes. The approach converts multivariate time series data into temporal multiplex recurrence networks and uses eigenvalue-based anomaly detection in addition to causal process graphs (CPGs). The framework is evaluated on a simulated pick-and-place production line with four failure scenarios. This contributes to a causal and transparent identification of root causes, which will be benchmarked against other methods in future work using real manufacturing data.

Keywords: Risk Management, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: Obsolescence is a major issue for industrial companies, as it can compromise the useful life of their systems. To mitigate its effects, it is crucial to manage it in advance. The obsolescence management process comprises several phases: preparation, identification, assessment, analysis and implementation. This article proposes a qualitative approach to the identification, assessment and analysis phases, known as the ARIANE method. ARIANE refers to “Analysis of Obsolescence Risks, Impacts, Criticality and their surveillANcE”. This method is fundamental to the systematic analysis of obsolescence risks, as it enables us to assess the potential impact and criticality of components within industrial systems. Its main objective is to assign an obsolescence exposure index to the problems identified, thus facilitating their prioritization and helping to guide the search for solutions during the analysis phase. In addition, the ARIANE method establishes appropriate monitoring mechanisms, ensuring proactive and effective obsolescence management over the long term. To illustrate the effectiveness of this approach, an example of application is presented through a radar monitoring system used in the aerospace industry in developing countries. This study highlights the ARIANE method’s ability to identify critical obsolescence risks and guide decision-making to ensure system sustainability.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Tool breakage monitoring (TBM) is a crucial technique for ensuring manufacturing process quality and safety. Despite the great potential of deep learning methods, data imbalance, and noise interference constrain their industrial application. This paper proposes a Semi-supervised Fusion Masked Autoencoder (FMAE) method for TBM in noisy conditions to address this. The constructed masked fitting distribution (MFD) module forces the model to focus on the global features of normal samples, better fitting the sample distribution and reducing noise impact. The designed fusion latent feature distance (FLD) loss function fuses the latent feature distance and the linear spatial distance between the data, enhancing the model's monitoring accuracy. The proposed method is applied to an experimental tool breakage dataset, and the findings indicate that FMAE has significant advantages in robustness and monitoring accuracy.

Keywords: Simulation technologies, Transportation Systems, Decision Support System
Abstract: This paper proposes a framework to implement a digital twin to simulate and optimize public transportation networks. The framework was applied to the bus system in the city of Lisbon, Portugal. By integrating digital twin and simulation technologies, inefficiencies in the city's bus network are identified and addressed. Data containing ticketing and General Transit Feed Specification (GTFS) is used to develop the digital twin model. Two different scenarios are implemented in the model, namely schedule adjustment and demand variations. The results of the experiments highlight improvements in occupancies of up to 22.9% and reductions in waiting times of up to 2.2 minutes.

Keywords: Heuristic and Metaheuristics, Transportation Systems
Abstract: The Mixed Capacitated Arc Routing Problem (MCARP) is a variant of the Capacitated Arc Routing Problem (CARP) which focuses on optimizing routes for vehicles with equal capacities, incorporating single and multiple direction arcs to better simulate real-world scenarios. This paper proposes a novel efficient local search algorithm for the MCARP, called Fast Late Acceptance Local Search (FLARE), the first local search applied directly to the giant tour representation in very large problems. The FLARE takes advantage of a reduced computational effort needed to calculate the cost of a solution by using a modified Split algorithm, which only calculates a small portion of the Split graph after a local search move. The proposed algorithm was tested on the widely used in the literature Hefei and Beijing benchmarks, with both having ten instances and sizes from 121 to 3584 tasks, and was able to reach better solutions in a given time limit.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Decision-support for human operators, Smart manufacturing systems
Abstract: Manufacturing increasingly demands flexible assembly processes that balance automation with human expertise. This paper addresses the challenge of optimizing pipe assembly layouts in a hybrid assembly scenario from aircraft manufacturing through a novel formulation as a multi-criteria shortest path problem. We introduce a process assistant system that integrates cognitive assistance through projection-based augmented reality with physical robotic support, maintaining worker decision-making autonomy throughout the assembly process. The underlying process optimizer, the core contribution of this work, uses a modified A* algorithm (MCA*) that simultaneously optimizes path length, material costs, and workspace ergonomics while satisfying physical and operational constraints inherent to our use case. Experimental validation across varying obstacle densities demonstrates that MCA* achieves superior balance across optimization criteria compared to traditional path-finding approaches while suitable for real-time assembly planning applications.

Keywords: Simulation technologies, Smart manufacturing systems, Industry 4.0
Abstract: In the context of Industry 4.0, simulation has emerged as a useful methodology for dealing with the complexity and dynamism of modern production systems. Simulating manufacturing processes is essential for optimizing the efficiency and flexibility of industrial systems. It enables the analysis and improvement of processes, serving as a key lever for innovation, optimization, and competitiveness in the Smart Factory era. This study focuses on the development of a simulation model for improving the Fischertechnik 4.0 Training Factory using AnyLogic® software. The model reproduces factory operations allowing risk-free testing of operational configurations and process improvements. By incorporating lean principles, the simulation model can identify and eliminate inefficiencies, streamline workflows, and reduce waste, leading to further enhancements in overall system performance. Key results reveal that targeted interventions significantly improve production performance, as evidenced by reductions in Production Lead Time, increases in Production Rhythm and Average Resource Utilization, and improvements in Overall Equipment Effectiveness (OEE).

Keywords: Design and reconfiguration of manufacturing systems, Decision Support System, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Airbus Helicopters uses simulation models to compute performance metrics such as delivery dates, investment, and work-in-progress. There are many parameters and scenarios to test, which require a lot of time to run and result in long computation times. This paper explores the replacing of simulation models with an AI-based approach. Synthetic data, generated through simulations and genetic algorithms, is used to train the AI model. Although the AI model provides faster predictions, it does not provide an explanation for its predictions. Clustering helps uncover patterns and interpret these predictions, making AI more transparent and applicable in industrial contexts.

Keywords: Sustainable Manufacturing
Abstract: The discovery and manufacture of new biopolymers necessarily require the availability of new precursors with reaction characteristics that distinguish them from those known at present. This paper investigates a new non-natural platform molecule termed 2,4-dihydroxybutyric acid (DHB) entirely obtained from non-edible carbon feedstock (syngas, molasses, lignocellulose) by microbial fermentation, as a building block to conceive, develop, and produce novel bio-based and biodegradable polymers. Different DHB precursors, through chemical and biotechnological process, can produce a wide panel of DHB-based (co)polymers harbouring performant properties in terms of thermomechanical, mechanical, rheological, thermal, and decomposition properties. A laboratory-scale life cycle assessment (LCA) to measure ex-ante environmental impacts of the DHB precursors, is presented in this paper. Results show that DHB-based polymers will have a reduced carbon footprint and lower energy consumption, compared to the existing bio-based polymers, due to the innovative biotechnological process used to produce them. Specifically, poly-DHB shows 30% reduction of CO2eq emission, compared e.g: to the existing polylactic acid (PLA).

Keywords: Sustainable Manufacturing
Abstract: This study explores how integrating data necessary for Life Cycle Assessment into the Digital Battery Passport enhances sustainability and supports circular economy principles in automotive marketplaces dedicated to the exchange of battery components and used batteries. By embedding standardized environmental indicators, the passport improves transparency, enabling stakeholders to assess environmental performance, prioritize sustainable practices, and support recycling and reuse efforts. Taking the perspective of a key player in the battery value chain, the study demonstrates how this integration enhances supply chain sustainability, provides accessible environmental information to customers, and fosters collaboration among stakeholders. Digital Battery Passport can act as a vital tool for advancing circular innovation and creating a more sustainable ecosystem for electric vehicle batteries.

Keywords: Industry 4.0, Optimisation Methods and Simulation Tools, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: : The Architecture, Engineering, and Construction (AEC) industry consumes considerable natural resources and energy with significant environmental impacts. To address these issues, the Life Cycle Assessment (LCA) methodology for buildings has been extensively researched as a means to evaluate the environmental impact of buildings. Central to this methodology are Life Cycle Inventory (LCI) databases, which serve as critical data sources for conducting LCA of buildings. This paper presents a novel approach to semantically extend and enrich an LCI database using knowledge graph technology, a form of Artificial intelligence (AI). By effectively supplementing and expanding the content of LCI databases, this approach aims to mitigate the uncertainties often arising from the lack of detailed material and process information during the early stages of building design. The enriched LCI knowledge base incorporates comprehensive data on building material classes, building component classes, building elements, construction solutions, and their associated environmental impact parameters. The semantic-enriched LCI knowledge base provides engineers and stakeholders with crucial information needed to evaluate the embodied environmental impact of buildings throughout the whole stages of building design.

Keywords: Supply Chain Management, Decision Support System, Industry 4.0
Abstract: With the proliferation of data in the digital era, Artificial Intelligence (AI) plays an increasingly important role in reducing human involvement and extracting information effectively and efficiently. The use of AI in Life Cycle Assessment (LCA)-related studies is growing. Drawing a big picture of this development helps inspire new research ideas. This study identifies the major research themes based on Cluster Analysis, considering the literature at the intersection of AI and LCA. Decision support for maintenance; Data, knowledge management, and real-time/intelligent systems; Industry 4.0, the Internet of Things, and new technologies; Digital twins, automation, and decision-making; AI explainability, implementation issues, and LCA software; Carbon footprint, energy efficiency, and sustainability; and Machine learning methods and algorithms for LCA are identified as the major research themes. Suggestions for future research conclude the article.

Keywords: Supply Chain Management, Operations Research, Industry 4.0
Abstract: Efficient warehouse management relies heavily on aligning storage policies with operational strategies to minimise travel distances and improve order-picking efficiency. While storage location assignment policies provide structured frameworks for inventory organization, their impact on picking route optimisation based on real-world settings remains underexplored. This study reviews the main storage policies reported in the literature and evaluates the performance of four storage policies—Random, Class-Based, Dedicated, and Hybrid—within a dynamic warehouse environment of a shoe manufacturing company. The analysis identifies their effects on travel distances, variability, and overall efficiency by systematically comparing these policies. The results demonstrate that Dedicated and Hybrid policies consistently outperform others, with the Hybrid policy showing particular promise in environments with high product variability due to its balance between flexibility and structure. The findings offer practical insights for addressing the storage location assignment problem (SLAP) and highlight the value of integrating storage strategies to enhance warehouse operations. This research contributes to the literature by offering a comprehensive evaluation of storage policies and emphasising their role in addressing the operational challenges of modern warehouses.

Keywords: Facility planning and materials handling, Industry 4.0
Abstract: Picker-to-parts e-Commerce fulfillment sections are still quite common for low-volume picking activities. This paper presents a design method to size such sections with the view to estimating their performance to help in bid design. A machine learning algorithm is trained to understand the impact of design, planning, and operational parameters on total pick distance. Numerical experiments with different machine learning algorithms are illustrated. The Random Forest, Decision Tree, Gradient Boost, XG Boost, LightGMB, CatBoost and a tuned Artificial Neural Network show the best performance in terms of error and fit. SHAP analysis shows that the picklist size, layout dimensions, seasonality, and the slotting algorithm are the features of the experimental study in descending order of importance. While this result may be specific to the data parameters chosen, it is important to use SHAP analysis to understand machine learning output.

Keywords: Human-Automation Integration, Robotics in manufacturing, Industry 4.0
Abstract: Unmanned Aerial Vehicles (UAVs) are valuable in logistics, especially for hard-to-reach areas. While much research focuses on UAV technology, their interaction with humans remains less explored. This study focuses on the technical feasibility of human-UAV interaction in a warehouse setting by designing a robust architecture. UAVs autonomously lead individuals to work stations, interacting via a keypad and sensor-based distance estimation. Interaction is recorded using RGB and motion-capture cameras. Results show that safe human-UAV interaction is feasible when UAVs maintain full situational awareness. A dataset of these interactions is made publicly available.

Keywords: Facility planning and materials handling, Smart manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Realistic data is the crucial basis for the development and operation of artificial intelligence (AI) applications. In intralogistics many applications require realistic 3D representations of the material flow systems as a basis. Capturing real world 3D data or building synthetic 3D scenes by hand is costly. In order to generate realistic scenes, a reference dataset of realistic intralogistics systems is required. This paper shows motivating use cases for a 3D dataset of intralogistics scenes and works out an approach to capture such a dataset.

Keywords: Smart transportation, Optimization and Control
Abstract: Efficient space utilization is a fundamental discipline in logistics, crucial for both storage and transportation of goods. The challenge of arranging goods geometrically to minimize empty space while maintaining stability is algorithmically represented by the three-dimensional bin packing problem. Existing literature predominantly focuses on volume minimization, neglecting other critical factors such as load stability, which is essential to prevent transport damage and associated costs. In this work, an approach based on meta reinforcement learning is presented, which enables the solution of multi-objective optimization problems in the 3D bin packing problem. The proposed solution and the training procedure are evaluated based on practical criteria, focusing on volume minimization and weight distribution. The experiments show that by using such methods, a flexible solution for the multi-objective 3D bin packing problem can be implemented.

Keywords: Decision-support for human operators, Heuristic and Metaheuristics, Probabilistic & statistical models in industrial plant control
Abstract: Operational decision-making in warehouse management involves executing short-term actions, often inherent to uncertainty. These uncertainties, present at the time decisions are made, can substantially impact the available information, leading to a reliance on heuristics for solving practice-oriented problems. As uncertainties evolve into deterministic information over time, the timing of decisions becomes crucial, balancing the need for sufficient implementation time against the degree of information accuracy available. The present work deals with how uncertainty is managed in a typical warehouse setting. It examines which routing strategies can be used to handle uncertainty and how stochastic information can be used to mitigate uncertainty.

Keywords: Production planning and scheduling, Smart manufacturing systems, Facility planning and materials handling
Abstract: This study is the first in the field of multi-crane scheduling (MCS) to address generalized temporal constraints, including task precedence relations and strict time windows for task execution. MCS focuses on assigning cranes to transportation tasks and deriving schedules that avoid crane interference. Motivated by a real-world steel plant in South Korea, we extend this to the multi-crane scheduling problem with temporal constraints (MCSPTC), where each task has precedence relations and time windows determined by the production plan. To solve the MCSPTC, we develop two mixed-integer linear programming (MILP) models based on existing approaches and compare their performance to identify the superior model. Additionally, we propose two schedule generation schemes (SGSs) that derive schedules quickly, providing a theoretical analysis of performance comparison and exploring their applicability in advanced algorithms like tree search. Both MILP models and SGSs are experimentally validated, with all experiments conducted based on real production data from the investigated plant.

Keywords: Industry 4.0, Smart transportation, Facility planning and materials handling
Abstract: Intralogistics plays a central role in manufacturing, encompassing the transportation, storage, and handling of materials within an organization. In recent years, smart intralogistics systems have emerged through the integration of smart technologies, which automate tasks or assist workers, offering significant potential to improve operational efficiency. This paper investigates the current adoption of smart intralogistics systems among seven high-precision technology manufacturers in Switzerland. A benchmarking study, involving on-site observations and semi-structured interviews, assessed their implemented technologies in intralogistics. The study reveals considerable variation in the number and degree of smart technology implementations, largely influenced by companies’ financial and human resources. The type of implemented technology depends on production volume and product complexity. Companies with high-mix, low-volume production favor solutions that enhance worker flexibility, such as wearable devices and smart tracking systems, while low-mix, high-volume manufacturers prioritize process automation through technologies like automated storage and retrieval systems (AS/RS) and automated guided vehicles (AGVs). Based on these insights, a five-step implementation guideline for smart intralogistics is provided to help companies seeking to embrace these technologies effectively.

Keywords: Facility planning and materials handling
Abstract: The key trade-off in selecting storage and order-picking systems lies between maximizing storage density, achieving high system throughput, and managing costs. Puzzle-based storage (PBS) systems excel in storing unit loads at extremely high density by eliminating the need for traditional aisles. However, these systems require each load to be placed on a moving device, such as a conveyor module or transport vehicle, making them costly to construct and maintain. Additionally, achieving high throughput in PBS systems poses a significant challenge due to their compact design and reliance on dynamic load movements. This paper focuses on a recently explored solution known as the Puzzle-Based Movable Rack (PBMR) system, which enhances traditional puzzle-based storage by equipping racks with autonomous wheels. While previous studies have explored the throughput potential of PBMR systems using analytical models, this study primarily contributes by conducting a detailed economic evaluation of PBMR systems under different throughput requirements. We assess their cost-effectiveness compared to conventional storage systems and analyze how system configurations impact both operational efficiency and feasibility. Our findings indicate that PBMR systems can achieve competitive throughput levels with optimized configurations while offering cost advantages in specific scenarios.

Keywords: Facility planning and materials handling, Optimisation Methods and Simulation Tools
Abstract: Multi-tote storage and retrieval autonomous mobile robot systems are new automated picking systems that feature mobile robots able to simultaneously carry multiple totes, fostering high efficiency and easily scalable layouts. This paper proposes an agent-based simulation model to estimate their performance and a numerical study that offers new design insights. Results show that robots and picking stations' configuration have significant impact on system performance. Specifically, adopting robots with more numerous trays for storing totes and equipment for the simultaneous unloading/loading of multiple totes at picking stations yields a reduction in the number of robots required to meet a given makespan.

Keywords: Design and reconfiguration of manufacturing systems, Industry 4.0, Smart manufacturing systems
Abstract: This study explores integrating blockchain technology into circular supply chain systems through a human-centered lens within the Industry 5.0 paradigm. Embedding transparency, trust, and collaboration into blockchain-enabled processes ensures alignment with sustainability and societal well-being. Key findings from a pharmaceutical case study highlight the role of human engagement and technological innovation in promoting circular supply chains. The proposed architecture leverages blockchain to enhance traceability, trust, and integration, while emphasizing human engagement and regulatory frameworks.

Keywords: Design and reconfiguration of manufacturing systems
Abstract: Reconfigurable Manufacturing Systems (RMS) offer the possibility of modifying their organisation to adapt to fluctuating demand. However, companies with RMS are still rare in industry and their reconfiguration potential is underworked. In the context of RODIC project, financed by the French research agency (ANR), we propose to show how an interdisciplinary approach, combining human factors and industrial engineering, represents an added value for understanding RMSs, in the context of Industry 5.0. We are proposing an original 3-stage methodology which begins with a measurement of the company's RMS degree of maturity through the Koren’s criteria; followed by an analysis of the tasks and the decisions made by the people in charge of the design of the reconfiguration of the system according to the Cognitive Work Analysis methodology; and finally, a comparison of the criteria and the carried-out activity. We applied this methodology to a company having an RMS. We can see that the company can be considered as RMS regarding its criteria and that the decisions made to reconfigure are directly linked to these criteria. These results provide a better understanding of what an RMS is and raise the question of a potential hierarchy of criteria. This study demonstrates the value of interdisciplinarity in studying this subject.

Keywords: Knowledge management in production
Abstract: In order to efficiently and easily manage knowledge within Small and Medium-sized Enterprises (SMEs), Personal Knowledge Management systems (PKMs) can be used. They enable individuals to formalize their experiences and knowledge using knowledge graphs called Personal Knowledge Graphs (PKG). To facilitate the sharing and dissemination of these individual knowledge repositories, an Organizational Knowledge Management system (OKMs) enables to gather several pieces of individual experiences or knowledge within Organizational Knowledge Graphs (OKG). This article proposes an approach for knowledge formalization and exploitation where graph nodes are transformed into vector embeddings and a learning mechanism enables to enrich them by the knowledge of their neighbors. Based on knowledge formalized by vectors, a second mechanism enables to identify relevant pieces of knowledge according to a new request. We applied our proposals in industrial problem-solving domain to recommend actors likely to work on new problems based on their fields of competence and their experiences. Our experiments show that it is possible to make better actor recommendations for new problems by reinforcing the knowledge of these actors through learning with knowledge that are semantically similar to that of the new problems.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Decision-support for human operators, Smart transportation
Abstract: Machine tools are essential to manufacturing for precise and efficient component production. With Industry 4.0, abundant machine condition data enables data-driven maintenance decisions. However, deploying condition-based maintenance solutions is challenging due to the diverse configurations of equipment, complex failure modes, and compatibility issues with the digital infrastructure. While machine tool health monitoring relies on detailed tests like Ballbar measurements, they consume valuable production time. To address these challenges, this article presents a human-centric development and deployment of a condition-based data-driven maintenance dashboard. The solution uses data from the controller system to improve machine tool testing in a Swedish heavy-duty vehicle powertrain facility.

Keywords: Industry 4.0, Decision Support System, Knowledge management in production
Abstract: Developing a highly competent workforce is essential for meeting the evolving demands of modern manufacturing. In this context, evaluating traditional and innovative training methods plays a critical role in enhancing the effectiveness of assembly processes. With a range of options—such as on-the-job training, classroom training and eXtended Reality solutions—it is critical to identify the most appropriate training approach for different contexts. Therefore, we performed a literature review and visited manufacturing companies to gain an overview of metrics involved in the assessment of training methods. To support this, we developed a comprehensive framework that guides the selection of such approaches. Our research identified key factors that contribute to training, which are integrated into the framework. The framework is designed to evolve alongside technological and contextual changes, allowing for ongoing adjustments as new strategies emerge or existing ones improve, such as decreasing Virtual Reality costs or personnel limitations impacting traditional training. This adaptability ensures the framework remains a reliable resource for making informed training decisions, tailored to specific needs while accounting for an ever-changing industrial landscape.

Keywords: Modelling Supply Chain Dynamics
Abstract: The survival of society depends on the survival and the viability of supply networks. Thus, advancing supply chain viability (SCV) as a research area has multiple facets in developing the prosperity of businesses and society as an integrated ecosystem. Therefore, to enhance the academic and practical understanding of SCV, this paper proposes a modeling approach and adds an extension overview to the already developed models of SCV in the literature. It adopts the control theory and dynamic capability view (DCV) to model SCV. Also, it highlights the important role of considering supply chains (SC) as dynamic systems that internally evolve and externally interact to deliver the desired output. The mathematical formulation and system-level modeling underpins this framework, offering insights into system stability, adaptability, and optimization. Ultimately, this study provides the needed materials for academics to guide their understanding of SCV modeling and frame the solutions to the mathematical model using the ordinary differential equations (ODE) technique to simulate the viable SC system.

Keywords: Supply chains and networks, Human-Automation Integration, Modelling Supply Chain Dynamics
Abstract: In recent years, unforeseen challenges have put significant stress on supply chains (SCs), with terms such as SC resilience and disruption gaining prominence. Foreseeable challenges can also threaten SCs, such as the ones associated with an aging workforce. This demographic trend is particularly pronounced in Europe and the USA, where labor shortages are expected to worsen unless mitigation strategies are implemented (e.g. new immigration policies). Initiatives like the European MAIA project emphasize the importance of supporting an aging workforce by developing methods and models to promote active participation1. Within the SCs, warehouses present unique challenges as they continue to rely heavily on human labor for physically demanding tasks. A shrinking and aging workforce results in longer processing times, negatively impacting service levels (SLs) across the SC. This study evaluates various scenarios, considering automation levels and workforce shortages over time. Using a real-world case study and simulations in anyLogistix, it assesses the impact of workforce availability and automation investments on service levels and costs. The results of this specific case study suggest that balancing automation and workforce investments is critical to maintaining competitive SLs and cost efficiency. These findings underscore the need for proactive, long-term strategies that enhance SC resilience, enabling companies to navigate disruptions and adapt to demographic changes in the evolving SC.

Keywords: Supply Chain Management, Inventory control, production planning and scheduling, Production Control, Control Systems
Abstract: Driven by the challenges posed by supply chain disruptions when demand surpasses supply, this study introduces a novel stochastic state-space approach to examine how the rationing game influences the reverse bullwhip effect and inventory fluctuations. The rationing mechanism is represented through a proportional controller, which regulates the allocation of goods to downstream supply chain participants. The analysis focuses on a three-node supply chain comprising a single distributor supplying two retailers. Each retailer manages its inventory using a base stock policy while responding to stochastic customer demand patterns. The dynamic behaviour of the supply chain is captured through a closed-form covariance matrix, which is formulated in terms of the proportional control parameters and the proportion of the distributor's inventory allocated to retailers. By analysing the model under stationary conditions, the study explores how distributor inventory variability and correlated demand patterns impact upstream demand amplification and broader instability phenomena within supply chains.

Keywords: Supply chains and networks, Production planning and scheduling, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: In an increasingly volatile global environment, onshoring has gained prominence as a strategic approach to enhance supply chain resilience, economic development, and sustainability. This study explores the implications of onshoring battery manufacturing in Australia, leveraging the country’s rich critical mineral resources. By utilizing a digital analytics model, the research examines the comparative benefits of onshoring and offshoring, focusing on key aspects such as supply chain efficiency, environmental sustainability, and strategic positioning. Onshoring is identified as a transformative strategy that minimizes reliance on foreign suppliers, strengthens domestic production capabilities, and aligns with stringent global Environmental, Social, and Governance (ESG) standards. It advances innovation, supports local industries, and reduces carbon emissions through localized manufacturing and logistics. In addition, onshoring improves supply chain resilience by eliminating risks linked with geopolitical tensions and global disruptions. This research indicates the potential for onshoring to drive Australia’s leadership in the battery industry while addressing challenges such as initial investment and scalability. The findings may deliver effective insights for industry stakeholders and policymakers, emphasizing the role of onshoring in advancing sustainable energy solutions and securing Australia’s position as a prominent player in the transition of the world towards renewable energy.

Keywords: Supply Chain Management, Supply chains and networks, Decision Support System
Abstract: Supply network resilience and responsiveness have become a critical topic due to multiple global supply network disruptions in recent years, ranging from natural to human-made hazards, e.g., earthquakes, the COVID-19 pandemic, and piracy in the Gulf of Africa. This research addresses two key aspects of overcoming these challenges: information sharing and supply network trust. Supply network participants need to be willing to share information in time and be able to anticipate and respond appropriately to disruptions. After introducing an extension to the Collaborative Auction Protocol (CAP), we present its framework, application, and validation. Next, we explore Demand-and-capacity sharing in supply networks, building on prior research (Seok and Nof, 2010; Ajidarma et al., 2022). Demand-and-capacity sharing was chosen as an implementation test for CAP research. One critical gap in the demand-and-capacity sharing protocol is the focus on timely demand fulfillment. There is a need for collaborative negotiations to uphold timely deliveries with economic efficiency. To address this, the study proposes developing a framework for Collaborative Negotiations in Demand-and-Capacity Sharing to enhance supply network efficiency and prevent opportunistic behavior. Finally, the improved CAP is evaluated through numerical examples and compared to the baseline and the first stage of CAP implementations. Results indicate that incorporating the supply network incentive factor enhances CAP's effectiveness in facilitating collaborative negotiations within supply networks.

Keywords: Supply Chain Management, Facility planning and materials handling, Operations Research
Abstract: Mobile additive manufacturing offers an agile, economical, and sustainable alternative to manage unexpected fluctuations in customer demand, especially in high-mix, low-volume settings. These mobile factories give flexibility and dynamism to the supply chain and decentralize it by creating the capability to produce on-demand and on-site. However, there are industries such as offshore oil platforms that cannot have these factories in situ, so distribution decisions need to be made to keep the benefits of mobile additive manufacturing. Drones represent a potential solution to deliver products to these offshore platforms. In this work, we proposed a mathematical model to analyze a supply chain composed of mobile factories equipped with additive manufacturing machines that supply spare parts to offshore platforms by delivering via drones or trucks/ships. The model is solved through a mathheuristic applied to a case study based on the operations of the company Fieldmade. The preliminary results emphasize the benefits of factory relocations and drone delivery to reduce lead times.

Keywords: Design and reconfiguration of manufacturing systems, Facility planning and materials handling, Operations Research
Abstract: The ideal automated material handling system needs to be cost-effective, flexible,scalable, and low-footprint. Uncrewed aerial vehicles (UAVs, or drones) present a viable alternative, offering affordability and dynamic routing without significant infrastructure. Our research applies mathematical models to route drones for material replenishment, demonstrating potential benefits and comparing it to a human-with-cart system.

Keywords: Supply Chain Management, Simulation technologies, Industry 4.0
Abstract: Creating factory layouts and digital models as a base for digital twins is traditionally a time-consuming, expensive, and complex process that requires significant IT expertise. Existing methods like 3D laser scanning are costly and can miss critical parts of manufacturing elements, leading to a high inhibition level. Therefore, building upon previous research, we have developed a method for generating 3D models of manufacturing and logistics systems using an affordable, commercially available UAV without mounting additional sensors or cameras. The workflow of the method can be separated into four steps: UAV-based data collection, point-cloud creation, 3D-modelling and automatic generation of a CAD layout. By leveraging the capabilities of the inbuilt camera of a low-cost UAV and photogrammetry techniques this method offers a cost-effective, timesaving and relatively simple alternative to traditional 3D scanning methods. In addition, the depicted method advances the transformation towards Industry 5.0 by enabling easier layout planning and process simulation as well as providing a scalable approach for the development of digital models, particularly for organizations with limited resources.

Keywords: Transportation Systems, Operations Research, Supply chains and networks
Abstract: The timely and efficient distribution of pharmaceuticals is a critical challenge in modern healthcare systems. This complexity intensifies when addressing the pickup and delivery of time-sensitive pharmaceutical items to moving customers, where each customer may be in different locations during the planning horizon. Thus, it necessitates innovative logistical solutions. This study introduces a system that integrates drones and motorcycles to optimize the routing and scheduling of pharmaceutical delivery operations. To achieve this, a mixed-integer programming approach is formulated to minimize transportation costs. The results of a case study demonstrate that the integration of drones leads to a 30.3% reduction in transportation costs while also achieving a 43.5% reduction in CO₂ emissions, underscoring both economic and environmental benefits. As per our findings, no prior study has employed drones for the distribution of time-sensitive pharmaceuticals to moving customers.

Keywords: Smart transportation
Abstract: The rapid growth of e-commerce has intensified the demand for efficient and sustainable urban logistics systems. This study introduces the two-echelon vehicle routing problem with autonomous delivery vehicles (2E-VRP-ADV) to optimize last-mile delivery by integrating conventional trucks in the first echelon and autonomous delivery vehicles in the second. A mixed-integer linear programming model is developed to minimize routing costs while considering ADV-specific parameters such as battery and load capacity constraints. Computational experiments on newly generated instances were performed, and sensitivity analyses revealed the impact of ADV battery and load capacities on delivery efficiency, providing actionable insights for improving environmental sustainability.

Keywords: Human-Automation Integration, Line Design and Balancing, Sustainable Manufacturing
Abstract: Human-centered technologies play a key role in the Industry 5.0 paradigm and can be the driver for greater diversity, equity and inclusion. This study explores the potential of cobots to promote the inclusion of workers with physical disabilities. Existing research on disability inclusion in manufacturing mainly focuses on sheltered workshops and manual assembly lines. Here, a Constraint Programming optimization model is proposed to balance a human-robot collaborative assembly line that includes workers with disabilities. Specifically, this research explores how cobots can support or complement workers facing task incompatibilities or longer execution times with a multi-objective approach considering cycle time, inclusivity, costs, and incentives. The findings show that when considering workers with same level of disability (i.e., same incompatibility ratio), but with different capabilities, the personal characteristics have a significant impact in a manual environment, while cobots help maintain line performance reducing the effect of individual abilities. However, this benefit comes with higher unit costs. The main contribution of this study is to elucidate the role of cobots in the trade-off between operational performance and inclusivity.

Keywords: Human-Automation Integration, Robotics in manufacturing, Decision Support System
Abstract: Cobots are increasingly used as co-workers, relieving human workers from performing physically demanding tasks, commonly encountered in assembly systems. Assembly processes generally consist of two primary tasks: the picking-and-placing of components and the actual assembly. While the latter adds value to the final product, the former does not and should be minimized wherever possible. This paper focuses on the integration of a cobot into an assembly station for the picking-and-placing of large and heavy components. Such components are often stored in locations far from the operator’s workstation, resulting in increased task execution times. A three-step methodological approach is proposed, considering the physical characteristics of the components, with the goal of minimizing the overall makespan by optimizing task allocation between human operators and the cobot through a mathematical model. The proposed approach is then applied to a case study, comparing a fully manual scenario with a collaborative scenario, achieving an 18.6% reduction in makespan in the collaborative scenario, thereby validating the industrial viability of the proposed methodology. Additionally, an economic feasibility analysis of the investment was conducted, resulting in a payback period of 2.24 years, which falls within the acceptable range for financial viability in industrial systems.

Keywords: Human-Automation Integration, Design and reconfiguration of manufacturing systems, Industry 4.0
Abstract: The design process of human-oriented collaborative workstations must balance production performance with workers’ psychophysical well-being and safety. Cobots enable the reduction of physical effort and hazardous tasks, favoring human efficiency in value-added activities. Nevertheless, the interaction between humans and cobots can generate side effects on mental strain, cognitive effort, and motivation, reducing the performance expected from collaborative setup deployment. Aiming to assess the cognitive stress developed in human-robot collaboration, this exploratory study proposes three laboratory scenarios with increasing interaction between the two entities (i.e., Independent, sequential and supportive). Biological parameters were monitored in real-time to evaluate participants’ physiological performance. The combination between decreased heart rate related features (RR distances for intra-operator comparisons and RMSSD for inter-operator comparisons) and increased number of electrodermal activity peaks suggested emotional arousal and/or physiological stress in presence of the cobot for the operator with lower confidence. Self-reported state and quantitative physiological signals for stress detection (i.e., heart rate variability and electrodermal activity) were analyzed to determine the correlation between interaction levels and cognitive ergonomics. Additionally, this research proposes a Human Digital Twin framework that includes real-time cognitive assessment from biological parameters to dynamically adapt collaborative robots’ modalities according to the abilities and confidence level of the co-worker.

Keywords: Industry 4.0, Human-Automation Integration, Robotics in manufacturing
Abstract: Order picking, a widely studied process in warehousing, is experiencing a growing implementation of automation. Among all the market-ready technologies, autonomous mobile robots (AMRs) have been receiving attention from both academics and practitioners lately. By utilizing AMRs, human pickers are relieved of the task of transporting items, typically performed using picking carts, giving rise to the term Assisted Order Picking Systems (AOPSs). However, the resulting changes in human work processes and the associated potential injury risks for humans working in these systems have largely been unaddressed so far. Caution is required due to the increased frequency of picking actions in AOPSs, which have been shown to be closely associated with potential musculoskeletal disorders. To address this research gap, we model the human work process in AOPSs, emphasizing task allocation within a homogenous human team and routing based on the multiple traveling salesman problem. Additionally, we consider human fatigue and develop a task allocation method that synchronizes human duty cycles within the wave picking strategy. Initial results from illustrative experiments demonstrate the impact of fatigue on the order picking makespan, albeit with the limitation of a simplified human-robot collaboration mechanism. Further issues related to model extensions and heuristic design are discussed in this paper and will be explored in future studies.

Keywords: Human-Automation Integration, Industry 4.0, Robotics in manufacturing
Abstract: Warehouses are responsible for managing inventory levels, handling order fulfilment, and preparing items for transportation, and their efficiency determines logistics performance. Over the last few decades, warehouses have been integrating robotic solutions to enhance operational efficiency and reduce costs. However, the impact of these technologies on human workers and overall system performance remains underexplored, especially considering that human-robot systems are sociotechnical systems, where humans and robots interact with each other and work together to achieve a joint outcome. This study therefore systematically reviews existing literature on human-robot collaboration (HRC) in a warehouse context using a sociotechnical lens, employing the framework developed by Neumann et al. (2021), to map interactions between technology, tasks, human factors, and outcomes. Our findings indicate that robots can alleviate physical workload, but that they also introduce challenges related to mental and psychosocial factors. We identify research gaps related to humanoid robots and economic evaluations of robot introductions. By adopting a sociotechnical lens, this work provides insights for researchers by summarizing the state-of-the-art of HRC in warehouses and pointing out research gaps. In addition, it provides important insights obtained in research that can be considered in managerial decision-making, helping practitioners in optimizing HRC in warehouses by balancing human and machine strengths.

Keywords: Human-Automation Integration, Line Design and Balancing, Robotics in manufacturing
Abstract: Even in modern production facilities, material supply processes for assembly systems, which have to process a wide variety of different components, are often characterized by manual activities. Kitting, as a supply strategy, improves part presentation at the assembly station but involves repetitive and physically fatigue tasks, particularly during material handling. In parallel environmental and social changes are forcing production companies to adopt more human-centric and sustainable approaches to material supply processes. However, supporting or automating these complex processes by automation technologies often introduces conflicting objectives: conventional automation demands standardized parts and processes, whereas modern assembly systems require greater flexibility in part supply. Human-robot collaboration in contrast offers the potential to resolve these conflicting demands while simultaneously enhancing employee well-being. Achieving this balance, however, necessitates innovative system designs and novel processes, tailored to the capabilities of both humans and robots. This paper presents a novel concept for a material supply process that integrates the kitting process into the assembly system O-cell. This integration aims to reduce human workload and the spatial requirements of the system while simultaneously improving efficiency and flexibility.

Keywords: Complex adaptive systems and emergent synthesis in manufacturing, Robotics in manufacturing, Human-Automation Integration
Abstract: Recent developments in Human-Robot Interaction (HRI) have moved beyond reactive, pre-programmed robot responses, aiming instead for collaborative systems where robots actively anticipate and adapt to human actions. By integrating Artificial Intelligence, robots can now interpret a range of human signals, enhancing the naturalness of interactions and making communication in industrial environments more intuitive. This evolution in robotic intelligence has expanded research to consider the cognitive aspects of robots. In industrial contexts, Human-Robot Collaboration (HRC) in shared physical workspaces has been extensively studied from the perspective of the human operator. However, there is a notable lack of research on the mutual cognition in interaction between humans and robots, who can act as a whole, intelligent system aimed at completing a task. This paper aims to explore the ontological foundations first and, then, the epistemological knowledge regarding the emerging patterns of evolved forms of HRC in industrial contexts involving physically shared workspaces. By transferring the concept of embodied cognition from other fields of study, the authors introduce and define the concept of Human-Robot Embodiment (HRE). This concept is characterized through descriptors that allow for evaluating, both in the design phase and during operations, the degree of mutual embodiment between the human and robotic partners. The advantages of the HRE approach are reflected in terms of workplace safety and ergonomics, task performance, and reliability.

Keywords: Industry 4.0, Human-Automation Integration, Smart manufacturing systems
Abstract: Human-Robot Collaboration (HRC) systems will shape industrial scenarios of the near future by integrating collaborative robots (cobots) to enhance performance and well-being of human workers. Despite the increasing research on this topic, investigating how human factors influence the success of HRC systems remains an issue to be further explored. This study reviews the role of human factors in various implementation stages of industrial HRC systems, including design, simulation, laboratory case studies, and industrial case studies. Findings reveal that most studies focus on controlled environments, highlighting gaps in real-world industrial applications where physiological and psychological factors remain overlooked. By addressing these gaps, this study provides insights into integrating human factors, fostering more inclusive industrial HRC systems that prioritize worker safety, ergonomics, ethics, and well-being.

Keywords: Robotics in manufacturing, Human-Automation Integration
Abstract: This paper presents an analysis of the impact of human and robot attributes on the performance of assembly tasks in collaborative Human-Robot Interaction (HRI). With the rise of Industry 4.0 and 5.0, the integration of collaborative robots (cobots) has become increasingly relevant. The paper explores how attributes such as operator age and experience, and robot size affect task efficiency and error rates. Through a systematic literature review (SLR) on 30 case studies, the correlation between these attributes and performance metrics were analysed. Results indicate that operator experience significantly reduces task time, while large robot size usually negatively influences both error rates and task adaptability. It is also provided a graphical visualization of the results which envelops all key elements of the HRI derived from the case studies’ analysis. The novelty of the work lies in the exploration of the interaction between human and robot attributes using a SLR, offering a deeper examination compared to previous studies. The findings highlight current research gaps and suggest directions for future studies, particularly on human factors, such as trust, safety and ergonomics.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Decision Support System, Industry 4.0
Abstract: With the coming of advanced immersive technologies such as augmented reality (AR) and virtual reality (VR), industrial maintenance processes are undergoing a deep transformation, with potential benefits in terms of efficiency, accuracy and reduced operational errors. However, the introduction of these tools raises fundamental questions about the impact of cognitive load on operators, such as mental overload which can compromise the safety or quality of performance and mental and physical well-being of personnel. This study proposes a systematic review of the existing literature to investigate the implications of cognitive load generated using AR and VR in industrial environments, with a particular focus on maintenance activities. The objective is to identify the most effective approaches to assess the impact of these technologies on the mental capacity of operators in real time. The analysis shows a wide spread of quantitative tools over qualitative ones, but also a marked heterogeneity in the methodologies adopted, which limits their standardization and comparability between studies. Through a thorough review, the study outlines a structured framework to validate existing methodologies and provide guidelines for sustainable integration of immersive solutions.

Keywords: Industry 4.0, Decision-support for human operators, Design and reconfiguration of manufacturing systems
Abstract: The transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) emphasises the need for human- centric industrial systems. In this context, maintenance operations represent a critical domain since they are inherently complex and directly related to the performance of production systems. According to the I5.0 perspective, a maintenance task allocation system should therefore be designed to optimise operator- task matches, minimising cognitive overloads and ensuring the operator’s well-being. However, current literature lacks a standardised framework for identifying which operator’s features significantly impact the proper accomplishment of maintenance tasks. This research therefore aims to understand the operator’s features that most influence the proper accomplishment of maintenance tasks within the I4.0 context. To achieve the objective of the present work, a two-phase methodology was employed: firstly, two Systematic Literature Reviews allowed for identifying a sample of maintenance tasks and operator’s features, and then a targeted survey was administered to professionals from companies with expertise in maintenance and I4.0 to understand which of the identified features are most relevant for the correct accomplishment of the analysed tasks. Although the study identified differences regarding the so-called cognitive and hybrid tasks, it generally demonstrated the importance of professional over individual features for the proper accomplishment of the identified maintenance tasks.

Keywords: Industry 4.0
Abstract: Virtual Reality (VR) provides unique opportunities for creating immersive, personalized and responsive learning environments through advanced features like hand and eye tracking. However, traditional VR training often lacks the flexibility to accommodate diverse learning styles. Personalization of training is crucial in industrial assembly, where user profiles and levels of expertise vary greatly. This paper introduces a novel solution for adaptive learning in VR focused on assembly knowledge training, using hand and eye tracking to deliver real-time feedback and individually adjusted learning paths. We applied our approach to two realistic assembly cases to evaluate its practical application. We hope to inspire future research further to explore and refine this adaptive approach, contributing to developing more flexible and effective VR-based training solutions for the manufacturing industry.

Keywords: Inventory control, production planning and scheduling, Decision-support for human operators, Optimization and Control
Abstract: This paper presents an integrated framework that optimizes manufacturing system efficiency by jointly considering the production plan, rotation strategy, and maintenance plan under stochastic demand and service level constraints. Rooted in the principles of Industry 4.0 and 5.0, this approach fosters a sustainable, resilient, and human-centric manufacturing ecosystem. The production plan incorporates operator-driven variability in production rates across a serial production line, ensuring demand fulfillment while maintaining optimal inventory levels. The rotation strategy aims to balance workload while protecting operator well-being by alternating operators between workstations with different levels of severity, thereby reducing prolonged exposure to high-risk tasks. This strategy helps prevent musculoskeletal disorders (MSDs) through systematic ergonomic assessments and risk quantifications. Additionally, the maintenance plan integrates periodic preventive actions and minimal corrective interventions to ensure machine availability while reducing maintenance costs. A case study in the garment industry demonstrates the effectiveness of the proposed framework in enhancing workload distribution, preserving operator health, achieving service levels, and optimizing maintenance expenditures.

Keywords: Supply Chain Management, Human-Automation Integration, Industry 4.0
Abstract: The black-box nature of Artificial Intelligence (AI) hinders stakeholder trust and comprehension, limiting practical adoption in business contexts. This study draws on findings from a case study in the agri-food sector applying Explainable AI (XAI) tools to support decision-making in case of SC disruptions. It proposes a trust-building framework that integrates ensemble learning models, interpretation tools (e.g., SHapley Additive exPlanations (SHAP), Large Language Models), interactive visualizations, and dynamic feedback mechanisms. These elements enhance transparency, enabling users to comprehend and act on AI-generated insights tailored to their expertise levels. By emphasizing human-AI collaboration, the framework addresses key gaps inaccessibility and usability, empowering diverse stakeholders to engage with and benefit from XAI systems. Copyright © 2025 IFAC

Keywords: Supply chains and networks, Supply Chain Management, Sustainable Manufacturing
Abstract: The transformation from linear to circular production is cornerstone for mastering sustainability. The fundamental aspect is that products from the utilization phase are fed back into the production phase, and a corresponding reverse supply chain exists for this purpose. Although various models for supply chain networks for value-retention circular economy strategies already exist in the scientific literature, there is a lack of decision rules for designing a suitable supply chain network. This paper aims to derive characteristics for selecting an appropriate supply chain network for value-retention circular economy strategies and to discuss rules of action based on case study research according to Yin.

Keywords: Supply Chain Management, Sustainable Manufacturing, Supply chains and networks
Abstract: In recent years, many companies have shifted from offshoring to backshoring, transforming global supply chains (GSCs). Environmental concerns, including degradation and emissions from long-distance transportation, have fueled demands for more sustainable supply chains (SCs). However, the role of environmental sustainability in backshoring—whether as a driver or barrier—remains unclear, as do its environmental outcomes. Using a multiple case study approach, this research analyzes secondary data from five European apparel companies employing backshoring. Findings reveal that while environmental sustainability is rarely the primary driver, it significantly improves post-backshoring performance. All five companies showed enhanced sustainability outcomes, highlighting a positive link between backshoring and environmental benefits. This study is of relevance not only to academia but also to practitioners and policymakers, offering insights to guide informed decisions aimed at making SCs more sustainable.

Keywords: Supply Chain Management, Optimisation Methods and Simulation Tools, Modelling Supply Chain Dynamics
Abstract: This paper proposes a hybrid architecture for supply chain coordination under bilateral information asymmetry and misreporting behaviors. By integrating a centralized Intelligent Mediator (IM) for data validation and credibility-based corrections with decentralized decision-making,the architecture stabilizes the system, which consequently leads to a reduction in variability. Simulation results across three scenarios demonstrate significant cost reductions and improved stability, highlighting the IM’s effectiveness in mitigating inefficiencies and enhancing supply chain performance.

Keywords: Supply Chain Management, Optimisation Methods and Simulation Tools, Modelling Supply Chain Dynamics
Abstract: This work addresses the strategic management of Supply Chains (SCs) by proposing a negotiation model between a manufacturer and a retailer to optimize SC coordination under information asymmetry. Focusing on reshoring as a key strategic decision, the model explores how trade-offs in operational constraints, cost variations, and lead times influence SC performance. By integrating reshoring considerations into the coordination framework, it offers a tool to support strategic decisions while enhancing SC collaboration. The study underscores the role of dynamic contracts in mitigating asymmetry and improving SC efficiency in the current dynamic and competitive manufacturing environment.

Keywords: Supply chains and networks, Supply Chain Management, Sustainable Manufacturing
Abstract: Reshoring represents a strategic choice by manufacturing companies in response to the changing dynamics of the global market. The motivations behind this choice can be traced back to complex industrial scenarios in which factors pertaining to different areas of a company's life are balanced. The effort made in this work starts from the consideration that, in order to provide support for strategic industrial decisions on reshoring, it is necessary to understand how, on the one hand, the demands of the production ecosystem operating on the decision maker are balanced and, on the other, the specific needs that push a company towards reshoring. In order to follow up on this research path, it was necessary to preliminarily define an ontology relating to the variables and quantities to be considered in order to build the causal model that will answer the question previously posed.

Keywords: Supply chains and networks, Optimisation Methods and Simulation Tools, Modelling Supply Chain Dynamics
Abstract: The submitted paper presents the development of a mathematical model to simulate the apple supply chain in Uruguay, with a focus on optimizing storage and commercialization strategies to maximize margins and minimize losses. The model addresses critical points such as the impact of labor quality during harvesting and the lack of loss measurement and control across the chain. Using a multi-objective approach, it balances the goals of margin maximization and loss minimization, implemented through the Pareto frontier via the Epsilon Constraint Method. Key findings include the importance of skilled labor, the direct sale of small apples to cider producers to reduce waste, and the need for accurate deterioration rate estimation and effective storage management. Sensitivity analyses highlight that while reducing losses is essential for profitability, prioritizing high-value customers can increase losses due to stringent quality requirements. The report provides strategic insights for improving supply chain efficiency and profitability in the apple sector.

Keywords: Operations Research, Industrial and applied mathematics for production, Supply Chain Management
Abstract: Abstract In alignment with the United Nations' Sustainable Development Goals, particularly the objective to secure clean, affordable, and sustainable energy" for all, this study embarks on a crucial exploration of alternative energy solutions amidst the global push for energy diversification and climate change mitigation. The utilisation of seaweed and agricultural waste as a bioenergy source stands out as a strategic opportunity to enhance environmental sustainability and foster renewable energy production within the framework of a circular bioeconomy. The core of this initiative's success hinges on informed decision-making in designing supply chains that are robust, efficient, and sustainable. This research introduces an innovative decision-making framework aimed at optimising the supply chain network for seaweeds and agricultural waste-tobioenergy conversion, with a particular focus on the decision analysis for selecting biorefinery locations. Employing an integrated approach that combines Geographic Information System (GIS) with Multi-Criteria Decision Making (MCDM), the study delineates optimal sites for biorefineries. This decision-making process meticulously considers both economic and environmental factors to propose a model that aligns with the principles of sustainability. Through this approach, the study seeks to influence stakeholders and investors by demonstrating the feasibility and attractiveness of bioenergy projects, thereby advancing the goals of the circular bioeconomy and contributing to the broader agenda of the sustainable energy transition.

Keywords: Supply Chain Management, Supply chains and networks
Abstract: Supply chain systems and logistics, play a key role in the transportation of goods in the industry. A buyer usually dispatches a request and a seller responds by sending the goods. However, this creates a massive overhead, due to geographic restrictions and cost of the logistics. For this reason companies often create systems that gather supplies in warehouses to support a number of buyers in the area. Thus, the notion of regional collector emerges. The collector essentially manages the sellers in the regions and responds to requests from the buyers. In this paper, we address this problem by creating zones that include sellers to create the zone collector. The zones are created based on a density that creates a community of sellers. The zone collectors, maximise their operations by using locality, in the sense that they collect the supplies from the sellers according to the demand of the buyers. Thus, we propose a solution based on junction tree, in order to increase the global utility function, We provide simulations to show the efficiency of our approach.

Keywords: Supply chains and networks, Supply Chain Management, Optimization and Control
Abstract: This study investigates the role of trust and collaboration costs in coalition-based supply chain network design under uncertainty. A bi-objective, two-stage robust optimization model is proposed to balance transportation and collaboration cost minimization with trust maximization, addressing the vulnerabilities of horizontal collaborations. By integrating an enhanced column-and-constraint generation algorithm, the study demonstrates that trust-intensive partial coalitions can achieve greater stability and cost efficiency compared to traditional grand coalitions. Computational experiments reveal optimal coalition sizes, strategic partner identification, and Pareto trade-offs between cost and trust, offering actionable insights for enhancing resilience and sustainability in collaborative supply chain networks.

Keywords: Supply Chain Management, Supply chains and networks
Abstract: This study addresses the prescriptive limitations of traditional DEA by proposing a novel hybrid framework, the Hybrid DEA-Stacking-Bayesian Model, to enhance supply chain optimization. An input-oriented DEA model under Variable Returns to Scale (VRS) is employed to minimize inputs while maintaining output levels. The efficiency scores obtained from the DEA model serve as outputs for machine learning models trained to predict the efficiency of decision-making units based on their input-output profiles. The hybrid approach integrates DEA with Stacking algorithms and incorporates Bayesian optimization for hyperparameter tuning. Results demonstrate that Bayesian optimization significantly enhances performance across all meta-learners, with XGBoost achieving the highest accuracy, improving R² from 0.56 to 0.92, and yielding the lowest error metrics. Comparative analyses highlight XGBoost as the best-performing model, followed by kNN, RF, and DT, confirming the effectiveness of the proposed framework. This work provides a robust decision-support tool for supply chain management, laying the groundwork for future research on supplier selection.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Probabilistic & statistical models in industrial plant control, Industry 4.0
Abstract: Predictive maintenance is a central concept in the shift towards Industry 4.0. Accurately estimating the remaining useful life of a machine, or a machine component, is an important aspect of predictive maintenance. Deep learning models have previously been applied to this task with success. However, these models may not perform well for cases where training data is sparse. In these situations, the model should also provide some degree of uncertainty about its prediction to instill trust in the user. Hence, predictive models should accurately estimate their own uncertainty, in addition to providing correct predictions. In this paper, we propose up-sampling of sparse ballbar measurement data in order to generate adequate samples to train and evaluate deep neural networks. The inference is conducted with three different types of models, Monte Carlo Dropout, variational inference, and deep ensemble. The approaches are compared based on point prediction accuracy, and uncertainty quantification quality. It is found that both Monte Carlo Dropout and deep ensemble perform well in regards to predictive accuracy, with the deep ensemble consistently resulting in the best calibrated uncertainty estimation.

Keywords: Decision Support System
Abstract: There is a need for improved digital decision support in civil engineering and administration of structures. This paper describes a conceptual digital decision support platform for lifetime extension of concrete structures aiming to close gaps regarding fragmentation of current decision tools and combining economic and environmental decision making for lifetime extension activities. A current version of the platform is used in the ongoing project Excon and the paper describes remaining work towards the end of the project, future improvements outside of the project timeline, and limitations regarding the methodology used in the platform development.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Decision Support System, Smart manufacturing systems
Abstract: This study presents a novel framework for tool wear prognostics in precision machining by leveraging a hybrid modeling approach that integrates physics-based principles and data-driven machine learning techniques. Specifically, we employ symbolic regression via genetic programming to develop recursive models that predict tool wear using both sensor-derived features and machining parameters. The goal of this study is to develop a generic recursive model for tool wear prognostics that can be applied across various machining scenarios. Two recursive models are proposed: a feature model that predicts health indicators based on cutting conditions and a tool wear model that uses the predicted health indicators as well as cutting conditions to estimate tool wear. The models were validated using the NASA milling dataset, which includes run-to-failure experiments on a Computer Numerical Control (CNC) machine with varying operating conditions. As a result, the developed recursive models demonstrated the ability to capture the dynamic nature of tool wear, while the recursive structure enabled continuous updates of predictions in real time. Furthermore, a generic recursive tool wear model was applicable across various machining scenarios, achieving a mean absolute error (MAE) of 0.048 mm, root mean squared error (RMSE) of 0.041 mm, and a R2 of 0.932 in tool wear predictions. This approach enhanced predictive accuracy and provided a generic tool wear monitoring solution applicable across different machines and operating conditions.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Human-Automation Integration, Smart manufacturing systems
Abstract: This pilot study examines augmented reality (AR) in dyadic remote maintenance from the perspective of on-site Maintenance Technicians (using AR headsets) and Remote Experts (using tablets or notebooks). A laboratory experiment was conducted to investigate differences in perceived cognitive load and usability, as well as potential correlations between these variables. Although no statistically significant group differences were found, the results suggest that Remote Experts tend to rate usability higher, while Maintenance Technicians report lower cognitive strain. Notably, a strong negative correlation was found between usability and cognitive load, highlighting the importance of user-friendly AR interfaces and well-designed tasks. The study also highlights the potential of AR to reduce travel costs and CO₂ emissions, enhance global knowledge exchange, and improve working conditions. Future research should include larger samples, real-world industrial environments, and deeper analyses of how role-specific requirements influence technology acceptance. Overall, the findings support the development of efficient and sustainable AR-based maintenance solutions for modern production settings.

Keywords: Decision-support for human operators, Modeling, simulation, control and monitoring of manufacturing processes, Optimization and Control
Abstract: This paper presents a Deep Q-Learning (DQL) framework to optimize maintenance and replacement strategies in multi-machine systems. Machine degradation is modeled with gamma and Poisson distributions, with transition probabilities integrated into the training process to enhance learning efficiency. Experiments show that the framework achieves fast convergence and high cumulative rewards. A state-action frequency analysis confirms alignment with system economic parameters, such as production revenue, maintenance costs, and failure penalties. The results demonstrate the scalability and practical applicability of the proposed approach to real-world industrial systems.

Keywords: Supply chains and networks, Decision Support System, Optimisation Methods and Simulation Tools
Abstract: Purpose The pharmaceutical industry is crucial for providing essential medications and vaccines, relying on cold chain logistics to maintain the integrity of temperature-sensitive products. The Covid-19 pandemic highlighted the need for effective management of these logistics. However, plastic waste and emissions from packaging pose significant environmental threats. While previous research has addressed various aspects of pharmaceutical supply chains (PSCs), there is limited focus on optimizing delivery lead times and using reusable packaging. This study aims to develop a multi-objective optimization model for a sustainable logistics network using reusable containers in the PSC, addressing high logistics costs, extended lead times, and environmental impacts. Research Approach The research involves a comprehensive modelling approach in collaboration with a UK-based cold chain solution company. A novel mathematical model is developed to minimize logistics costs, lead times, and carbon emissions. It optimizes container movement and inventory levels, considering constraints like transportation modes, supply and demand, hub capacities, and container washing processes. The model is validated using real-world data from the industry partner and solved using the epsilon constraint method to find trade-offs among objectives. Sensitivity analysis assesses the impact of input parameters on model performance. Findings and Originality The proposed model improves logistics effectiveness, reduces costs, and lowers environmental impacts. It uniquely addresses the complexities of reusable container logistics in the PSC, including bi-directional container flows, varying customer bases, multiple transportation modes, and geographically dispersed hubs. This holistic approach enhances the sustainability of cold chain logistics in the pharmaceutical industry. Research and Practical Impact This research advances sustainable PSC literature, focusing on the pharmaceutical sector, with a model that integrates real-world data for relevance to current challenges. The study provides a tool to improve cold chain logistics' sustainability and efficiency, offering cost savings, better customer service and reduced environmental impact.

Keywords: Sustainable Manufacturing, Decision-support for human operators, Smart manufacturing systems
Abstract: The automotive industry increasingly embraces repair practices driven by sustainability trends shaped by government policies and consumer preferences. Artificial intelligence (AI) enhances repair processes through versatile applications like computer vision. However, its energy demands and carbon footprint have recently garnered attention. Image data preprocessing techniques applied during the data derivation stage offer solutions to reduce AI’s environmental impact. This paper presents novel preprocessing strategies implemented during the data collection phase to address challenges in applying computer vision within the automotive repair industry. The study balances the trade-off between deployment benefits and energy demands, particularly focusing on Fault Detection Models.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Optimisation Methods and Simulation Tools, Supply Chain Management
Abstract: The agricultural sector is vital for income and employment in many developing countries but faces challenges like climate change, water scarcity, land degradation, and biodiversity loss. It also significantly contributes to global greenhouse gas (GHG) emissions, producing around 6.5 billion metric tons of carbon dioxide equivalent (GtCO₂e) in 2023, 11% of total GHG emissions. Coffee, a major global commodity, sees South America, particularly Brazil and Colombia, as leading producers. Colombia, responsible for 0.4% of global GHG emissions in 2020, sees 71.3% of its emissions from agriculture and land use. As coffee demand grows, sustainable practices are essential to mitigate climate change impacts. This study, in collaboration with Expocafe, a major Colombian coffee exporter, aims to develop a decarbonization model for the coffee industry. It focuses on quantifying GHG emissions from chemical fertilizers used in coffee farms and identifying effective mitigation strategies. Data from 32 coffee farms was collected, including production areas, types of fertilizers used, application timings, and land-use practices. Current fertilizers include NPK compounds, ammonium nitrate, urea, and lime. Expocafe explores reducing GHG emissions by introducing an environmentally friendly fertilizer called “Nitrosoil.” The study examines various factors like cultivated area, production yield, fertilizer application rates, and soil N₂O emissions to create different scenarios for reducing emissions. Initial findings indicate that alternative fertilizers can significantly reduce GHG emissions in the coffee supply chain. This research is unique in quantifying the GHG emissions of chemical fertilizer production and use, considering both carbon stock and biomass. It addresses a gap in understanding GHG emissions from various fertilizers and their production processes. The study's insights will aid farmers, cooperatives, Expocafe, and other stakeholders in making data-driven decisions to enhance the sustainability of coffee production. By understanding the impact of different fertilizers and application intensities, stakeholders can design policies to mitigate GHG emissions effectively.

Keywords: Sustainable Manufacturing, Business Process Modeling

Keywords: Production planning and scheduling, Supply Chain Management, Industry 4.0
Abstract: In response to current environmental concerns, this paper addresses the integration of sustainable production and delivery scheduling using a Just-in-Time (JIT) approach. The focus is on minimizing both delivery delays and CO2 emissions in a job shop environment with hybrid fleets of electric and combustion vehicles. A mixed-integer linear programming (MILP) model is developed to optimize production schedules and delivery routes under strict time windows and energy constraints. Using this approach, the study explores the trade-offs between environmental impact and operational efficiency. The experimental study validates the model while identifying its limitations and highlighting areas for further improvement. Results show that the proposed model achieves optimal solutions within up to 24 hours, particularly for medium-sized instances.

Keywords: Sustainable Manufacturing, Industry 4.0, Business Process Modeling
Abstract: With the shift towards a circular economy (CE) recognised as a key driver for advancing sustainable development, researchers and practitioners are intensifying their efforts to implement CE solutions with the support of Industry 4.0 digital technologies (DTs). However, there is a risk that some could focus solely on implementing DTs and thereby lose sight of the overall goal of sustainable development. To prevent this, the research in this paper describes an intelligent circular transition framework that addresses important opportunities identified by a systematic literature review (SLR): (1) the need to consider interactions from a systems lifecycle perspective, (2) the evaluation of CE principles based on the Triple Bottom Line (TBL), and (3) the use of DTs as enablers. The Plan-Do-Check-Act (PDCA) cycle is employed as the foundational methodology, as it is essential to approach this transition as a continuous improvement process, viewing it as a cyclical progression rather than a linear path. A high-level conceptual framework to align circular strategy with digital maturity is proposed, along with future research opportunities to strengthen and enhance its generalisability.

Keywords: Supply chains and networks, Industry 4.0
Abstract: The circular economy has increasingly become mainstream in the attempt to address environmental concerns and manage and eliminate waste for the conservation of finite resources. Nevertheless, due to real-world complexities, the transition from a linear to a circular economy is neither straightforward nor smooth. Thus, a mix of linear and circular practices prevails globally. Through the exemplification of an illustrative real-time outdoor power product, this exploratory study aims to identify the practical challenges in transitioning to a circular economy. The findings of this study provide an in-depth understanding for practitioners to consider and manage the challenges of the circular economy transition.

Keywords: Sustainable Manufacturing, Industry 4.0, Smart manufacturing systems
Abstract: The circular economy (CE) is an essential concept towards achieving sustainable development, with digital technologies being one of the key driving factors. In particular, the end-of-life (EoL) stage of a product requires careful consideration since how a product is handled when it reaches its EoL significantly impacts the overall sustainability performance. Digital technologies, such as blockchain, have demonstrated significant potential in addressing critical EoL challenges, including providing proof of origin for proper handling and ensuring traceability of product performance. This paper explores blockchain-based EoL strategies, including remanufacturing, refurbishing, and demanufacturing, to improve these processes and contribute to a more sustainable CE. Key findings highlight blockchain’s potential application in various EoL strategies. Additional strategies, such as refuse, repurpose, and rethink, are also discussed. Finally, possible avenues for future research are summarised.

Keywords: Industry 4.0, Design and reconfiguration of manufacturing systems, Sustainable Manufacturing
Abstract: The convergence of Circular Economy (CE) strategies and Industry 4.0 (I4.0) technologies has influenced the entire product lifecycle enhancing sustainability and operational efficiency. Simultaneously, Customer Service and Maintenance (CSM) serve as critical pillars of organizational growth, fostering customer loyalty and business success. However, the intersection of these domains remains underexplored. This paper addresses this gap by introducing Circular Customer Service and Maintenance 4.0 (CCSM4.0), an extension of the Reference Architecture Model for Industry 4.0 (RAMI4.0), designed to enhance product circularity and optimize CSM operations. RAMI4.0 is a highly recognized reference architecture that presents a structured approach for implementing I4.0 technologies. The CCSM4.0 framework integrates key activities, including proactive and reactive solutions, I4.0 technologies, and CE strategies. This integration aligns with the hierarchical manufacturing levels (e.g., product, workstation, company, external) and functional layers (e.g., asset, integration, information, business) defined in the RAMI4.0 framework. By analyzing real-world applications, this study demonstrates that CCSM4.0 can extend product lifespan and operational efficiency while improving decision-making through advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing (CC). The insights provided aim to guide industrial practitioners in implementing CCSM4.0, offering a practical pathway to achieving sustainable manufacturing and superior CSM operations.

Keywords: Supply Chain Management, Decision Support System, Operations Research
Abstract: Purpose. This article aims to examine the obstacles and challenges related to dynamic changes in the logistics sector and the need for innovation before the logistics 4.0 transformation. In addition, this study provides an overview of innovation deployment in transport and warehousing companies in EU countries by company size. Research Approach. This study used a two-stage methodology. First, a comprehensive review of the scientific literature was conducted to identify the challenges of the logistics sector and the reasons that encourage transport and warehousing companies SMEs to innovate or refrain from the solution. Secondly, a bibliometric analysis was performed with the keywords "logistics", "logistics 4.0", "innovations", "technology", and "digitalization". Findings and Originality. Theoretically and empirically identify possible obstacles and challenges related to the inevitable changes in the logistics sector, and the need for innovation for transport and warehousing companies, which arose before the logistics 4.0 transformation, is already lacking at the desired level. Research Impact. The article contributes to developing the logistics sector and the development of the theory of innovation in transport and warehousing companies, as it analyzes SMEs' unique challenges and solutions in implementing innovations in companies of different sizes. The study reveals how implementing innovations in transport and warehousing companies contributes to the ability of companies in the logistics sector to compete in the market, including in a global context. The study shows how innovation helps small and medium-sized companies keep up with large competitors, exploit market opportunities, and achieve sustainability goals. Practical Impact. This work is valuable from a practical point of view; it helps transport and warehousing companies in the logistics sector understand whether the implementation of innovations is necessary due to market pressure and competition or if it is inevitable due to global changes and the article also analyzes which innovations have the most significant impact on the activities of SMEs (digitalization, application of artificial intelligence, green technology integration). The study results

Keywords: Enterprise modelling, integration and networking, Complex adaptive systems and emergent synthesis in manufacturing, Transportation Systems

Keywords: Supply Chain Management, Industry 4.0
Abstract: This article examines the obstacles and challenges related to dynamic changes in the logistics sector and the need for innovation ahead of the Logistics 4.0 transformation. It provides an overview of innovation deployment in transport and warehousing companies across EU countries, categorized by company size. A two-stage research methodology was employed: first, a comprehensive review of scientific literature to identify the key challenges faced by companies in the logistics sector; second including an analysis of logistics companies’ efficiency, Data Envelopment Analysis (DEA) results, and efficiency scores. The findings highlight the theoretical and empirical gaps in understanding the challenges transport and warehousing companies face before the transition to Logistics 4.0. The article contributes to logistics sector theory and innovation practices by examining how logistics companies address unique challenges in implementing innovations Practical insights are provided to help companies determine whether innovation is driven by market pressure or global changes. The study identifies impactful innovations such as digitalization, artificial intelligence, and green technologies, and suggests recommendations for companies support programs to foster innovation in the logistics sector.

Keywords: Knowledge management in production
Abstract: This paper explores how the doctoral-level course IFEL 8001: Research-Based Innovation for Engineers at NTNU addresses critical gaps in the university’s broader innovation ecosystem. While NTNU emphasizes innovation through research, education, and industry collaboration, challenges such as limited interdisciplinary integration, fragmented innovation processes, and weak alignment between doctoral education and industry needs persist. The study analyses six NTNU innovation reports and course presentations to evaluate IFEL 8001’s role in bridging these gaps. The analysis focuses on the course’s structure, its integration of participants’ ongoing research, and the practical competencies it imparts to PhD candidates. The course succeeds in equipping PhD candidates with essential innovation skills often overlooked in traditional doctoral programs. These include intellectual property management, iterative prototyping, and technology transfer. By incorporating real-world projects as case studies, IFEL 8001 fosters practical skills, industry relevance, and interdisciplinary collaboration, addressing systemic weaknesses in NTNU’s innovation ecosystem. IFEL 8001 demonstrates how structured, practice-oriented doctoral education can overcome institutional gaps in innovation training. It enhances engineering innovation capacity and prepares PhD candidates to drive interdisciplinary solutions and industry-academia partnerships, strengthening NTNU’s role as a leader in research-based innovation.

Keywords: Human-Automation Integration, Industry 4.0, Robotics in manufacturing
Abstract: Human–robot collaboration (HRC) seeks to enable cooperative tasks between robots and humans. This approach leverages human flexibility and intelligence in complex tasks with the robots’ endurance and precision in repetitive and heavy tasks. The goal is to enhance productivity, flexibility, and quality while reducing employees’ physical workload. HRC aims to positively impact sustainability goals and corporate resilience. A comprehensive understanding of these developments necessitates a socio-technical perspective on HRI in intralogistics to identify opportunities and barriers to effective robot implementation and human interaction. This multimethod study is focused on pinpointing these opportunities and barriers from a socio-technical perspective, emphasizing human factors as per the Industry 5.0 vision. Additionally, a framework was developed and validated to guide future research on HRI in intralogistics, with a focus on human behavior. General barriers to HRI in intralogistics were also identified.

Keywords: Inventory control, production planning and scheduling, Industry 4.0, Robotics in manufacturing
Abstract: Managing inventories is associated with high costs, which may account for about a third of the total logistics costs. These costs arise from different factors such as the consequences of Inventory Record Inaccuracy (IRI). IRI represents the discrepancies between the physical and digital inventories. These discrepancies generate great labor efforts to solve them, along with write-offs and oversells. This leads to economic and productivity losses. To reduce the impact of IRI, inventories are periodically controlled for audit compliance and correct the physical-digital discrepancies. This is usually done through costly, time-consuming, and labor-intensive approaches. The recent advances in drones have led to their adoption for logistic purposes, including inventory monitoring. Drone-based inventory monitoring entails several benefits compared to conventional approaches such as being automated and quicker. This may result in better accuracy and performance, contributing to operational excellence. Despite this, available literature mainly focuses on the technical and conceptual development of drone-based inventory monitoring systems. Less interest has been devoted to evaluating their economic viability compared to conventional labor-intensive approaches, particularly considering in the analysis reduction in labor and IRI-related issues. To this end, this work aims to develop a mathematical model to compare drone-based inventory monitoring with the labor-intensive conventional technique, considering the presence of write-offs and oversells. The model is tested on two case studies: a manufacturer and a third-party logistics (3PL). Warehouse managers may exploit the model for preliminary assessment of the economic benefits of drone-based inventory monitoring.

Keywords: Transportation Systems, Facility planning and materials handling, Smart transportation
Abstract: Automated Guided Vehicles (AGVs) are autonomous material handling transport systems used for a wide range of applications in several industrial contexts. In the years, different AGVs systems have been developed, relying on different technologies. Among the most widely used and developed AGVs systems there are the Laser Guided Vehicles (LGVs) and the Autonomous Mobile Robots (AMRs) ones. In a warehouse, AGVs are used for supporting material handling activities, especially for the putting away and picking tasks. This research originates from some case studies of companies involved in handling and distributing fresh food products, within large-scale organized distribution networks. In this context, operations such as receiving, picking, and distributing goods must often be carried out within extremely tight timeslots. Products arrive from various suppliers on single product load units and must be quickly distributed to form mixed pallets to be sent to the different served stores. In the considered case, the creation of mixed pallets is done by four robotic picking stations working in parallel. In each station a robot picks the required number of boxes from the single product pallets and sorts them to create the mixed pallets. The focus of this research is to evaluate different automated solutions for the supply of the single product pallets to the four robotic picking stations, considering the possibility of employing either LGVs or AMRs, to evaluate their performances and understand their most proper applicability fields, according to different order profiles. The performance and the effectiveness of the supply alternatives strongly depend on the characteristics of the order profiles and, then, on how the single product pallets are distributed to the stores. To evaluate the performance of the three supply alternatives for each one of the four scenarios, we implement a simulation model using FlexSim Software 2024 v2. This preliminary study clearly opens to the possibility of further investigating these solutions, by comparing them from a performance perspective as well as from an economic one, also taking into account that the same picking rate can be reached by a different number of LGVs or AMRs.

Keywords: Industry 4.0, Supply Chain Management, Human-Automation Integration
Abstract: In the contemporary global business landscape, the rising complexity of production and logistics systems increases the need for automation. That is why investments in warehouse automation are rising, and practitioners need guidelines to invest in the right technology. In this paper, a systematic literature review is performed to assess the contributions' ability to provide an up-to-date framework for automated order picking systems (OPS). To adopt an empirical lens, technology providers were interviewed about the best available technologies on the market, identified through technology scouting, to provide more details on the distinct features of the classified technologies. The study aims to provide a new framework for automated OPS, grounded in literature, to help practitioners better navigate the available technologies and choose those that best suit their industrial context. Future research should evaluate automated OPS technologies more holistically and quantitatively, including different perspectives from providers, to better analyse requirements, and impacts on macro-trends.

Keywords: Facility planning and materials handling, Production Control, Control Systems, Optimization and Control
Abstract: High-throughput sorting facilities require significant investment in costly resources, including labor, space, and equipment. The rapid grow of e-commerce, coupled with economic and technological advancements in recent decades, has intensified the demand for sorting technologies with increased throughput and improved cost/performance ratio. In this paper, we investigate sorting systems that utilize grids of four-way conveyors (4WCs), where items can move freely in up to four cardinal directions from each array cell to its neighbors. In such systems, items enter the grid at designated input cells and are transferred in a sequence of steps to their target output cells and eventually to destination bins. The grid-based approach enables multiple items to move simultaneously, offering the potential for significantly higher throughput compared to traditional conveyor-based sorting technologies. However, operating grid-based sorting systems requires solving complex, real-time, parallel decision-making challenges. A naive, myopic operational policy is susceptible to possible deadlocks, where items block each other from reaching their destinations. To address this, we propose a novel centralized online algorithm that ensures deadlock-free operation. The algorithm models item movements within the grid using a time-expanded graph (TEG). Upon an item's arrival, its route and schedule are determined, and the graph is immediately updated to preclude conflict with future items. Extensive experiments demonstrate that our centralized algorithm achieves higher throughput than previously proposed distributed algorithms. Moreover, it scales effectively, enabling real-time operation of large grids with thousands of cells, making it suitable for modern high-throughput sorting facilities.

Keywords: Smart manufacturing systems, Decision Support System, Industry 4.0
Abstract: In Engineer-To-Order industries, precise estimating of manufacturing time is crucial for optimizing the operations lead time, managing costs and overall efficiency. This study presents a new approach to enhance the reliability of manufacturing time estimations by utilizing a hybrid machine learning chain composed of supervised, unsupervised, and memory-based learning components. This work makes two main contributions: (i) identifying critical features that impact time estimation across product families, followed by an automated classification system using Neural Networks to categorize product families based on these features and (ii) a structured retrieval process that leverages KD-Tree to improve the efficiency of retrieving relevant historical cases. This new approach was applied on a real-world case in a French metallurgy industry, where it achieved enhanced Mean Absolute Error in time estimates compared to previous methods, underscoring its effectiveness.

Keywords: Human-Automation Integration, Smart manufacturing systems, Industry 4.0
Abstract: The introduction of human robot collaboration (HRC) in manufacturing operations has the potential to enhance both efficiency and flexibility by effectively coordinating the distinct capabilities of each team member. However, enabling accurate interaction and collaboration between humans and robots in complex manufacturing environments presents significant challenges. This paper proposes a multimodal large model, aiming to enhance robot understanding of human intentions to improve the accuracy of HRC in complex manufacturing environments. An integrated framework, consisting of a physical layer, multimodal information fusion large model layer, virtual layer, and service layer, is designed to enhance HRC in manufacturing operations. Experiments are conducted using vision, audio, and electroencephalogram (EEG) signals, all derived from the same spatiotemporal context within a human robot disassembly setting, to validate the proposed approach. Experiment results show that the proposed multimodal large model can significantly enhance HRC with a 96.7% accuracy, compared to 88.8% accuracy with the vision model, 66.6% accuracy with the audio model, and 30.4% accuracy with the EEG model. This study provides an effective approach for enhancing robot understanding of human intentions in human robot collaborative manufacturing (HRCM), even with limited sample data.

Keywords: Supply chains and networks, Knowledge management in production, Modelling Supply Chain Dynamics
Abstract: Suppliers play a critical role in the efficient functioning of supply chains, and any risks associated with them can significantly impact supply chain performance. While numerous studies have developed ontologies for various supplier-related areas, there is a lack of focus on ontologies specifically addressing supplier risk management. In addition, the construction of ontologies has mainly relied on approaches which are time-consuming and resource-intensive. This paper bridges this gap with two major contributions: (i) A new methodology for ontology development that combines a Large Language Model (LLM) and a human expert to efficiently extract and organize domain knowledge from academic literature and (ii) A new supplier risk management ontology that formalizes knowledge related to supplier risk management. To evaluate its effectiveness, the proposed ontology is compared with one developed by a human expert to assess its completeness and accuracy.

Keywords: Human-Automation Integration, Sustainable Manufacturing
Abstract: The integration of human-centric principles into manufacturing is a key aspect of Industry 5.0, yet existing research lacks a structured approach to measuring its impact on manufacturing viability. This study conducts a systematic literature review to analyze how human-centric strategies—such as worker empowerment, collaboration, and human-AI interaction—are reflected in viability Key Performance Indicators (KPIs). Unlike previous studies that examine viability in broad terms, our review systematically links human-centric principles to measurable KPIs, offering a novel perspective on resilience, adaptability, and sustainability in manufacturing. The findings reveal key gaps in the literature and provide a framework for integrating human-centricity into viability assessments, serving as a foundation for future empirical research and practical applications in manufacturing system design.

Keywords: Decision Support System, Industry 4.0, Design and reconfiguration of manufacturing systems
Abstract: The increasing complexity of the aerospace industry has highlighted the necessity to fathom the entire product lifecycle. From initial conceptualization, engineers are meant to consider the production, maintenance, and decommissioning phases of an aircraft. By identifying how design decisions made in the conceptual phase impact the other systems involved, overall development time, costs, and quality can be significantly improved. Over the past decades, digital models have been employed in the design of complex systems, the management of data integration, and the formalization of domain knowledge. This paper examines the application of an ontology-based engineering methodology to support early concurrent engineering driven by knowledge.

Keywords: Supply Chain Management, Supply chains and networks
Abstract: In this paper, we discuss the study of optimal policies for retail platforms to set information-sharing fees and for duopoly suppliers to respond to online platform's information sharing offers through a game-theoretic approach. We analyze the results of four sub-games and derive the optimal acceptance/rejection policy principle. Our research indicates that even if the retail platform does not charge any fees for information sharing, it benefits all supply chain members. However, suppliers do not always accept the offer of information sharing as the corresponding fee increases. In addition, the information asymmetry problem can be addressed when the information cost is not too large. More management insights will be discussed in this paper.

Keywords: Production planning and scheduling, Heuristic and Metaheuristics, Transportation Systems
Abstract: The integration of production scheduling (PS) and vehicle routing problems (VRP) is crucial for achieving operational efficiency in today's competitive market, yet these problems have often been studied in isolation. A lack of integration can result in increased costs, such as longer lead times and higher holding costs, ultimately affecting customer satisfaction. To address these challenges, this study develops an integrated PS and VRP model aimed at minimizing logistical costs, including inventory holding and fixed vehicle usage costs, as well as penalties for earliness and lateness in deliveries. The proposed model considers multiple customers with varying demands, due dates, and batch sizes, requiring efficient scheduling, production, and timely delivery. The objective is to balance customer satisfaction by minimizing both early and late delivery penalties while optimizing production scheduling to reduce inventory holding costs. An Improved Genetic Algorithm (IGA) is employed to solve the integrated problem, with parameter tuning performed to enhance the balance between exploration and exploitation. The results demonstrate that the IGA achieves superior convergence and solution quality compared to conventional approaches.

Keywords: Inventory control, production planning and scheduling, Industry 4.0, Decision Support System
Abstract: The real-time monitoring of equipment health enables the implementation of condition-based maintenance (CBM), which serves to mitigate the risk of unexpected failures. The availability of CBM information provides Advance Demand Information (ADI), which can be leveraged for improving spare parts management. Effective spare parts inventory management necessitates a balance between the availability of spare parts and the associated costs of ownership and management. A shortage of spare parts can disrupt maintenance, which reduces service levels and potentially results in customer loss. Conversely, excess inventory leads to high holding costs. This balance is typically achieved through the implementation of inventory policies that use historical data to find optimal parameters. However, research on the use of real-time degradation data for the spare parts decision-making process remains limited. Al Khoury et al. (2024) proposed a Proactive Base Stock Policy (ProBSP) that exploits real-time degradation data by proactively ordering spare parts in anticipation for future needs. As a result, the ProBSP requires jointly optimizing the initial stock level and the order threshold to achieve the required service levels while keeping stock levels at a minimum. The ProBSP has demonstrated substantial savings in stockholding costs (up to 67%), emphasizing the significance of leveraging degradation data in spare parts control. Deep Reinforcement Learning (DRL) is a powerful technique to learn good policies for sequential decision problems, such as inventory problems. DRL has shown promising results in inventory control problems (De Moor et al., 2022; Van Hezewijk et al., 2023). Nevertheless, DRL has not been adopted to incorporate CBM data in spare parts inventory control problems in case of multiple machines and stochastic lead times. In this research, we adopt state-of-the-art DRL algorithms to develop spare parts ordering policies that leverage degradation data for systems with multiple machines and stochastic spare parts lead time.

Keywords: Inventory control, production planning and scheduling
Abstract: Food waste is a lost value in grocery retailing. Preventive measures depend on the product's salability period, i.e., shelf life, and the respective inventory policy. Standardizing the former is a waste prevention measure. Minimum quality acceptance criteria for the suppliers enforce such measure. Besides the salability period, inventory policy effectivity depends on demand, customer tendency to pick the freshest product in store, and replenishment constraints. This study ranks the impact of these dependencies on inventory policy effectiveness. Demand, service level, and customer-picking behavior have a notable impact. Besides efforts towards shelf-life extensions, the study suggests integrating the implications of the inventory planning policy to reduce food waste in grocery retail.

Keywords: Operations Research, Inventory control, production planning and scheduling
Abstract: We consider a lot-sizing problem motivated by liquefied natural gas (LNG). LNG is an alternative fuel for road and maritime transport.

It can be supplied from alternative sources with different quality and prices. LNG is subject to quality deterioration. It is yet possible to upgrade its quality by mixing lots with different levels of quality. LNG is provided to customers via special-purpose facilities. We address the lot-sizing problem in such a facility. The problem entails finding a minimum-cost replenishment plan satisfying demands over a finite planning horizon, while meeting a minimum quality level. This is a non-linear and non-convex mixed integer problem that cannot be tackled effectively by off-the-shelf solvers. To overcome the computational challenge, we develop an efficient heuristic. We numerically illustrate that our heuristic yields high-quality solutions for practical-sized instances within very reasonable computational times.

Keywords: Supply Chain Management, Operations Research, Risk Management
Abstract: Supply chain resilience is essential for maintaining global economic stability and ensuring the continuous delivery of goods and services. This study introduces a new decision analytics framework to stress test supply chains by improving robustness, addressing network vulnerabilities, and developing adaptive mitigation strategies. The proposed approach includes a modified Time to Survive (TTS) model, a sustainable mitigation planning Time to Recover (TTR) model, and a sequential Monte Carlo simulation to measure variability in mitigation plans. It focuses on reducing business, environmental, and social impacts while supporting strategies such as dual sourcing and capacity reallocation. Multi-objective mathematical programming and open-source technologies are used to develop the framework. Computational experiments with a synthetic supply chain network generator confirm its scalability, efficiency, and practical value.

Keywords: Supply chains and networks, Risk Management, Robustness analysis
Abstract: In the field of manufacturing strategy and supply chain management, navigating disruptions and so-called “black swan events” is essential within today’s dynamic and often unpredictable business environment. Yet, while research usually emphasizes the negative impacts of demand disturbances, less attention has been given to disruptions that result in exceptional increases in demand. We analyze application cases in manufacturing reporting such positive exceptional demand disruptions and their embedded management challenges. Despite their positive effects on profit at the beginning, these disruptions – referred to here as “silver swan events” – pose similar severe threats to corporate and supply chain health, paralleling the risks associated with negative disruptions such as the well-known black swan events. We propose this new typology of “silver swan events” as a critical yet underexplored category of supply chain disruptions, where further research and interest is highly warranted in order to enable strategic manufacturing planning.

Keywords: Supply Chain Management, Risk Management
Abstract: Supply chain resilience (SCR) is critical due to global disruptions. This study examines how big data (BD) adoption, resources, and predictive analytics (BDPA) improve SCR. Using data from 183 companies in France and PLS-SEM analysis, the results show that business uncertainty and data-driven strategies drive BD adoption. High-quality BD improves BDPA capabilities, which strengthen SCR. Decision maker intuition also influences the BDPA-SCR relationship. The study provides insights for practitioners to leverage BD and predictive analytics for better decision making and risk mitigation in volatile supply chains.

Keywords: Supply Chain Management, Risk Management, Industry 4.0
Abstract: Artificial Intelligence (AI), Big Data Analytics (BDA), Blockchain (BLC) and Digital Twin (DT)/Simulation have the potential to significantly improve the resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of advanced digital technologies in the context of supply chain resilience, research to date is dispersed and fragmented. We address and synthesize this dispersed knowledge by conducting a systematic literature review of AI, BDA, BLC, and DT/simulation research in supply chain resilience that has been published in impact-factrored journals between 2014 and 2024, inclusively. 29 journal articles were identified as primary papers relevant to this research. The findings advance the domain knowledge by (i) assessing the current state of AI, BDA, BLC and DT/simulation adoption in supply chain resilience literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, redesign) that addressed digital tools have been reported to improve, and (iii) synthesizing the reported benefits of addressed digital tools in the context of supply chain resilience.

Keywords: Quality management, Design and reconfiguration of manufacturing systems, Smart manufacturing systems
Abstract: This paper presents a novel, low-cost, and accessible AI-based quality control test bench solution for the textile industry. It is especially designed for small and medium-sized enterprises. Using available consumer hardware and innovative AI techniques, the proposed system offers highly accurate quality control without significant financial investment. The developed software libraries, hardware documentation, and design are published as open-source projects on GitHub. The proposed approach is intended to empower small and medium-sized enterprises to install and implement advanced automatic quality control at low cost. The effectiveness of this approach has been validated within the Köstler Green-AI Hub pilot project.

Keywords: Sustainable Manufacturing, Simulation technologies, Discrete event systems in manufacturing
Abstract: Recycling of materials and products is a central pillar of the circular economy, which is embodied in re- and demanufacturing processes. Recently, re- and demanufacturing have gained significant attention from both industry and academia. Despite the high demand and interest, research is still needed to optimize the processes and overcome drawbacks and challenges before market adoption. With the rise of Industry 4.0 technologies, the concept of Digital Twin (DT) has been extensively used in different applications to support decision-making, simulate the operations and production environments. DT is primarily used throughout the product life cycle from design to operation, lacking application in the end-of-life phases. Research is needed to cope with the additional variability inherent in the different features of each product which adds more complexity in simulating demanufacturing environments. Also, the available data and case studies are still limited. This study provides an overview on the applications and challenges of DT in the re- and demanufacturing and differentiates between commonly used frameworks in such fields through multiple research questions to be tackled in future studies. In the current study, the claims are also tested on a real system in a re- and demanufacturing lab.

Keywords: Sustainable Manufacturing, Decision Support System
Abstract: As industries embrace circular production to increase sustainability, effective methods for managing End-of-Life (EOL) products have become increasingly critical. This paper introduces a novel approach to support decision-making in the handling of returned products, focusing on the recovery and refurbishment of components for reuse in remanufacturing. The proposed method utilizes BOM data to assess the probability of component reusability and calculates the economic viability of disassembly and refurbishment. By conducting evaluations at both the product and component levels, the framework facilitates precise and efficient grading of EOL products.

Keywords: Supply chains and networks, Supply Chain Management, Decision Support System
Abstract: In recent years, unstoppable developments in technology have caused radical transformations all over the world. With the introduction of spatial computing technologies such as virtual reality (VR), digital twin, augmented reality (AR), and mixed reality (MR), Metaverse has become one of the most popular topics in the business world, and people frequently talk about it. Metaverse has transformed the service provided to customers to a completely different dimension, a mixture of the real world and the virtual world, causing a change in the customers' understanding of service quality. For this reason, this study focuses on how to measure metaverse service quality. For this purpose, a generic model is proposed in the study based on a literature survey and the opinions of industrial experts. The proposed model's validity, applicability, and suitability have been tested with an application in Turkey's supply chain area. The criteria weights of the proposed model within the scope of this application were calculated using the analytical hierarchy process (AHP) method, which is one of the multi-criteria decision-making methods. However, real-life problems based on expert opinions contain complexity and uncertainty, and in addition, human perception in decision-making processes brings intuition and hesitation. For this reason, the classical AHP method was expanded to interval-valued intuitionistic fuzzy (IVIF) sets. The study's results reveal that immersive interaction and experience dimension is a priority, and supply chains should primarily consider issues such as customer centricity, blockchain-enabled traceability, and social interaction to increase their metaverse service quality performance.

Keywords: Sustainable Manufacturing, Decision-support for human operators, Quality management
Abstract: The growing emphasis on circular economy principles has highlighted the importance of efficient remanufacturing processes in reducing waste and maximizing resource utilization. However, uncertainties in reverse logistics—specifically related to the quality, quantity, and timing of returned products—pose significant challenges for achieving defect-free remanufacturing. This paper proposes a Decision Support System (DSS) based on decision tree machine learning models to address uncertainties in remanufacturing. The DSS analyses key variables such as return timing and product condition to optimize remanufacturing outcomes by incentivizing early returns and improving product quality and return predictability. Additionally, the system integrates Zero Defect Manufacturing (ZDM) principles by leveraging real-time data and predictive analytics to minimize defects in remanufactured products. The paper discusses the implications of this approach for managing reverse logistics uncertainties and outlines future research directions for refining and implementing the DSS in practical settings. By bridging predictive analytics with sustainable manufacturing practices, the proposed framework contributes to more efficient and resilient remanufacturing systems within the circular economy.

Keywords: Heuristic and Metaheuristics, Scheduling, Optimization and Control
Abstract: The Distributed Permutation Flow Shop scheduling Problem with Worker Flexibility is a newly proposed topic in the shop scheduling field. It represents a complex scheduling challenge that arises in modern manufacturing environments where jobs must be assigned to multiple production lines distributed across different locations. This complexity increases with the consideration of worker flexibility, where workers can be reassigned across various tasks and machines, impacting overall efficiency. In this study, we propose the application of the Iterated Greedy algorithm to solve the Distributed Permutation Flow Shop scheduling Problem with Worker Flexibility with the objective of minimizing the makespan among all factories. The experimental results demonstrate that the Iterated Greedy algorithm can generate high-quality solutions within reasonable computational times, proving its effectiveness in addressing the complexities of Distributed Permutation Flow Shop scheduling Problem with Worker Flexibility. Moreover, the study highlights the critical role of worker flexibility in influencing scheduling efficiency.

Keywords: Human-Automation Integration, Simulation technologies, Robotics in manufacturing
Abstract: This study addresses the high unemployment rates among people with disabilities by exploring task assignment and load balancing in manufacturing work systems to enhance accessibility, inclusivity, and productivity. With a particular focus on the decreasing availability of physically demanding manufacturing and blue-collar jobs, the paper addresses how emerging technologies, in particular collaborative robots (cobots), can reduce work demands and facilitate a balanced workload tailored to physical and cognitive parameters. It introduces a three-dimensional conceptual model that includes the human, the work system, and the assistant system, highlighting an assembly line scenario with a cobot as an assistant system. The model is optimized through task sharing, demonstrating a strategic approach to enhance inclusivity in work systems. Validation of the proposed model is achieved through a simulation that illustrates how cobot integration can balance workloads and maintain productivity while addressing barriers in work systems.

Keywords: Distributed systems and multi-agents technologies, Design and reconfiguration of manufacturing systems, Human-Automation Integration
Abstract: The coordination of heterogeneous robotic systems presents significant challenges in industrial automation, particularly in enabling seamless communication, efficient task allocation, and adaptability across diverse robot types. This research proposes a framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance multi-robot collaboration. Unlike existing approaches, this framework enables natural language-based communication among robots and camera agents while leveraging MQTT-based messaging for scalable, event-driven communication. This approach reduces reliance on predefined control protocols and enhances real-time adaptability in multi-agent environments. The LLM dynamically generates low-level task sequences, assigns tasks in real time, and facilitates adaptive multi-agent communication. A prototype evaluation focuses on task delegation, analyzing how the LLM interprets user commands, generates task allocations, and assigns agents for execution. The results demonstrate that the proposed framework effectively automates task allocation and ensures precise delegation to the appropriate agents, supporting scalable and resilient automation in next-generation smart manufacturing.

Keywords: Sustainable Manufacturing, Robotics in manufacturing, Industry 4.0
Abstract: Efficient disassembly and maintenance of battery packs are critical for sustainable practices and extending battery lifespans, particularly as second-life usage and re-purposing, such as traction batteries for home power storage, gain interest. This paper presents a novel Human-Robot Collaboration (HRC) system designed to address the challenges of disassembling battery packs using a collaborative robot and advanced human-machine interfaces. Key features include a projected interface for task visualization and user guidance, CAD-driven localization for robotic unscrewing and screwing tasks, and the development of an accurate process for cutting welding seams. By integrating these technologies, the system aims to enhance the efficiency, safety, and precision of collaborative battery disassembly, offering economically viable methods for battery maintenance and reuse within the circular economy. This work addresses the current industrial gap in semi-automatic solutions for the disassembly and maintenance of Li-ion battery systems and contributes to advancing productivity in re-manufacturing processes. Evaluation results confirm the applicability of the approach in a real scenario, and revealed optimization potential.

Keywords: Quality management, Human-Automation Integration, Smart manufacturing systems
Abstract: Although factories are increasingly automated nowadays, operations still require human intervention due to complex operations for a machine to operate or the investment not being economically viable. Many human-operated activities require a lot of physical effort, unnatural positions, and physical demands. In addition to repetitive work, bad positions or physical effort can cause long-term problems for operators. There are different ways to help operators, for example, through breaks or relaxation exercises, which help avoid or mitigate some of these health problems in the long term. This work presents use cases in the DIH4CPS project that established a solution for collecting data from workers and advising workers and the company's human resources on the best strategies to keep workers safe and fit at work and home. Two different use cases were defined to achieve these objectives, and two different solutions were developed. Having been validated in three different factories to monitor workstations with different characteristics, aiming to improve solutions and check how they react in different environments. This paper presents the problem and the results obtained from validation performed in the different factories.

Keywords: Supply Chain Management, Production Control, Control Systems, Knowledge management in production
Abstract: In the high dynamism of today’s market, Supply Chains (SC) face multiple disruptions. Therefore, SC paradigms have undergone significant transformations to address these challenges. Supply Chain 5.0 (SC5.0), as a new human-driven paradigm, integrates advanced technology with three core principles to enhance Supply Chain Performance (SCP). Supply Chain Performance Measurement Systems (SCPMS) represent key tools for measuring SCP and managing SC operations. SC5.0, as a novel discipline, has revealed the shortcomings of classical SCPMS in measuring SCP, calling for more focused research in the field. The aim of this paper is to examine the shortage of research on SCPMS for SC5.0. To that end, a systematic literature review is conducted to identify and analyze existing SCPMS and their interactions with the SC5.0 principles. Findings reveal that SCPMS has undergone significant developments, both in approaches, and in the metrics adopted. However, there is still no specific SCPMS designed to measure the performance of SC5.0. The findings can assist scholars in understanding the current state of SCPMS in literature. Future research can focus more on developing SCPMS that are able to address all SC5.0 principles.

Keywords: Supply chains and networks, Supply Chain Management, Operations Research
Abstract: Current vehicle routing methods suffer from non-linear scaling with increasing problem size and are therefore bound to limited geographic areas to compute results in time for day-to-day operations. This only allows for local optima in routing and leaves global optimization potential untouched. We develop a reinforcement learning model to approximately solve the three-dimensional loading capacitated vehicle routing problem in linear time. We demonstrate the linear time scaling of our reinforcement learning model and benchmark our routing performance against state-of-the-art methods. Our results show a promising first step towards large-scale logistics optimization with reinforcement learning. Copyright © 2025 IFAC

Keywords: Supply Chain Management, Supply chains and networks
Abstract: Supply chains are dynamic systems with constantly changing data, necessitating adaptive machine learning models. While prior research emphasizes integrating new data to enhance decision-making, the need to remove obsolete or harmful data from models remains underexplored. This paper addresses the challenge of efficiently removing undesired data, such as bankrupt suppliers or data errors, from already trained machine learning models via machine unlearning. We highlight supply chain challenges that can be addressed by machine unlearning, introduce a new unlearning method for practitioners that works without any parameter tuning, and demonstrate its effectiveness in a supply chain error unlearning case study.

Keywords: Supply Chain Management
Abstract: Large language models (LLMs) have garnered significant attention since OpenAI launched ChatGPT in November 2022, demonstrating immense potential across various domains, including education, healthcare, software engineering, and supply chain management (SCM). We further explore the potential of LLMs in SCM and examine the challenges associated with their adoption. Additionally, we present a pipeline case study to demonstrate the integration of LLMs with a decentralized agent-based system to effectively execute SCM tasks. By exploring these opportunities and providing a delivery prediction delay example, this study would inspire further leverage of LLMs for enhanced SCM operations. This study thus contributes to the growing body of research on the transformative impact of LLMs in the real-world SCM context.

Keywords: Supply Chain Management, Risk Management, Industry 4.0
Abstract: In recent years, the growing number of disruptions across industries has driven researchers to explore the potential of artificial intelligence tools in proactively predicting supply chain risks. A key area of focus has been the use of machine learning and deep learning algorithms to predict supplier punctuality, particularly given the importance of anticipating late deliveries for companies that implement just-in-time or lean manufacturing strategies. However, existing studies have primarily examined the ability of these tools to make deterministic predictions, leaving a gap in understanding their capacity to provide probabilistic predictions in this domain. This paper addresses this gap through a case study investigation in the automotive sector, where the performance of traditional, machine learning, and deep learning models in making probabilistic predictions have been compared. Specifically, accuracy metrics such as coverage probability, sharpness, and interval score have been computed for the different class of models in the examined case study for short term and long-term forecasting horizons. Additionally, the models were assessed in terms of training time and storage requirements, providing a comprehensive comparison of their practical implementation.

Keywords: Discrete event systems in manufacturing, Smart manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: This paper presents a novel supervisor-based architecture for designing a reliable logic controller, which integrates the cyclic operation of programmable logic controllers (PLCs) with the formal advantages of supervisory control theory (SCT). By combining these two paradigms, this architecture delivers a comprehensive approach to control and monitoring, overcoming the inherent challenges associated with synchronization issues. Through abstractions in task modeling, sensor integration, and constraint handling, this integrated framework resolves complexities, ensuring seamless interaction between control and monitoring processes. A proof-of-Concept is presented, demonstrating the effectiveness of this integrated approach in a real-world context.

Keywords: Knowledge management in production, Modeling, simulation, control and monitoring of manufacturing processes, Optimization and Control
Abstract: Model Predictive Control (MPC) is a multivariable control and optimization technology widely applied in various industries. The paper offers an alternative approach, which employs "point" models developed at each time step. To create such models, a data mining algorithm is used, which addresses the associative database of inductive knowledge. This knowledge is obtained by extracting patterns from data by means of intelligent analysis. The associative search algorithm calculates the coefficients of the predictive model and the control actions simultaneously at each time step. The algorithm for calculating these coefficients and actions one or more steps ahead is proposed.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Smart transportation, Simulation technologies
Abstract: Identification of the dynamics model of a boat is considered. The requirements of inland waterways are taken into account. Models of motion direction dynamics and translational velocity are studied. Transfer functions from rudder control to motion direction angle and from throttle value to translational velocity are identified by a finite-frequency approach, where sinusoidal test signals are used. A 3 DOF nonlinear model of the boat dynamics is identified via a tool for greybox parameters estimation.

Keywords: Supply Chain Management, Supply chains and networks, Optimization and Control
Abstract: This paper introduces a conceptual dynamic model designed to analyze logistics ecosystems with limited resources. The research focuses on mathematical modeling of logistics facilities, such as container terminals. It examines strategies for optimizing resource distribution among system components to meet specified demands and explores the role of digitization and emerging technologies, including artificial intelligence, in enhancing the resilience and efficiency of supply chains. Emphasis is placed on the critical importance of data integration, improved collaboration between supply chain participants, and the adoption of digital solutions to increase transparency and operational efficiency within logistics systems.

Keywords: Production planning and scheduling, Inventory control, production planning and scheduling, Facility planning and materials handling
Abstract: Operations management (OM) is crucial for improving hospital efficiency and ensuring timely, cost-effective, high-quality healthcare. Despite progress in the field, existing literature offers only a fragmented view. This paper aims to address this gap by conducting a scoping review to provide a comprehensive overview of current research and establish the groundwork for future exploration. The review analyses OM domains and subdomains, offering an overview of the topics studied within them and highlighting their evolution over time. The review reveals that current hospital operations management research is largely focused on capacity and demand management, technology adoption, and uses modelling as the research methodology. However, areas like diagnostic services, pharmacy, chronic care, and support services are highlighting gap areas. Emerging domains like Technology in OM and Process Management also offer research opportunities.

Keywords: Human-Automation Integration, Decision-support for human operators, Supply Chain Management
Abstract: The increasing healthcare demands and recent global health crises highlight the critical need for agile, resilient, and tech-driven supply chains. [2],[4],[5]. Advancements in healthcare supply chains, especially through automation and digital technologies, can transform healthcare delivery by boosting product quality, enhancing supply chain transparency, and optimizing efficiency [1], [3]. As healthcare supply chains continue to evolve, embracing innovation is crucial for improving patient outcomes, reducing costs, and enhancing overall healthcare system resilience. This study presents a framework for the adoption of AI technology based on the results of a use case analysis.

Although AI is very promising, resistance and trust of workers and patients towards using these technologies, data security, data and privacy protection, emotional intelligence, high cost of investment, clinical errors and safety issues are reported challenges/barriers [11], [6], [7], [9], [10].

The development and deployment of AI based systems involves a variety of design considerations that address the unique challenges and opportunities within healthcare. As outlined by [12], clinical and non-clinical decision-making related considerations are shown below:

 Data-Driven Foundations  Accuracy and Performance  Ethical Considerations  User-Centered Design  Scalability and Flexibility  Privacy and Security  Regulatory Compliance  Ethical Deployment and Decision Support

Incorporating these design characteristics, this study presents a framework for successful implementation of AI systems that can be used by healthcare organizations considering the use of automation and machine learning in the clinical and nonclinical administrative decision tasks to improve health outcomes of patient quality, patient safety, and staff burnout.

Keywords: Human-automation integration, decision-support fort human operators, decision support systems, AI technologies, healthcare.

Keywords: Decision Support System, Optimization and Control, Heuristic and Metaheuristics
Abstract: This study presents a hybrid framework that integrates machine learning and a metaheuristic optimization algorithm to address patient prioritization and vaccine queue management challenges in healthcare environments. A machine learning model classifies patients using health and demographic data, ensuring equitable prioritization, while the metaheuristic optimization approach focuses on reducing wait times and improving resource allocation. Experimental results using French open data demonstrate the framework's capability to balance efficiency and equity, showcasing its potential for pandemic preparedness and broader applications in healthcare resource management. This adaptable solution offers a scalable method to enhance healthcare delivery in resource-constrained settings.

Keywords: Optimisation Methods and Simulation Tools, Industry 4.0, Sustainable Manufacturing
Abstract: There has been extensive research in the healthcare sector on the three paradigms of 'lean', 'green' and 'smart', separately or in dual combination. However, the link between these three concepts has not yet been well explored in the literature and is in need of development. The main purpose of this paper is to explore the integration of the Smart Green Lean (SGL) model in the healthcare sector and to validate its effectiveness and its sustainability with the use of simulation tools. Based on the literature, this study highlights the relevance of the SGL model in the healthcare sector. It also addresses the lack of literature on this emerging theme, particularly in relation to simulation methodologies. The authors propose an integrated theoretical framework based on the results of the literature on the use of digital tools, integrating several factors related to the 3 concepts of Smart, Green and Lean, with an assessment of the impact on Key Performance Indicators (KPIs). This study highlights the lack of literature on the subject and provides new insights to address it.

Keywords: Scheduling, Heuristic and Metaheuristics, Simulation technologies
Abstract: Surgeon fatigue has a well-documented impact on performance and patient outcomes, yet many existing surgery scheduling models inadequately address rest-time requirements. This study presents a dynamic rest-time scheduling method that optimizes surgical schedules by balancing operational efficiency with surgeon well-being. In this regard, a heuristic is developed that dynamically calculates rest periods based on surgery durations, besides employing a Genetic Algorithm (GA) to refine schedules. Moreover, a simulation-based evaluation assesses the quality of the proposed schedules, considering factors such as adherence, delays, fatigue, and operating room utilization. Results demonstrate significant improvements over baseline models, with reduced surgeon fatigue, better schedule adherence, and enhanced operating room efficiency. The proposed framework also incorporates visualizations to give stakeholders actionable insights into resource allocation and fatigue management.

Keywords: Supply chains and networks, Modelling Supply Chain Dynamics
Abstract: Platform-based manufacturing, as a relatively new but more and more popular concept, is clearly changing the traditional ways of production. However, the requirements of the customers did not change: one of the most important KPIs is still delivery time accuracy. The paper investigates the concept of platform-based manufacturing from the perspective of different manufacturer strategies. It explores when it is beneficial to prioritize reliability by reserving resources to handle unforeseen events that may cause delivery delays. Additionally, it examines the circumstances that favor companies focused on maximizing machine hours (while placing less emphasis on delivery accuracy and reliability). In the paper, the performance of the companies themselves is in focus, and after introducing a platform-based collaboration structure, different scenarios are tested and evaluated using agent-based simulation. The main novelty of the research is that the strategies for companies working through a platform have not been examined so far, especially focusing on their reliability and its effects. In addition, papers investigating platform-based manufacturing from other perspectives often neglect capacity constraints of the companies, which is also included here.

Keywords: Risk Management, Sustainable Manufacturing, Decision Support System
Abstract: Obsolescence does not only affect physical or electronic components but also encompasses skills and documentary resources essential for the maintenance and operation of critical systems. This article introduces a new method to assess obsolescence risks in complex and long life-cycle systems by incorporating often-overlooked dimensions such as human skills and technical documentation. The proposed method is based on identifying critical subsystems, necessary skills, and documentation, followed by the use of matrices to evaluate dependencies and associated obsolescence risks. This approach enables the calculation of an overall risk, taking into account the system's complexity and the criticality of each element. A practical case study illustrates how this method can identify major vulnerabilities and prioritize corrective actions to ensure system sustainability. This method provides a robust analytical framework to anticipate obsolescence risks and effectively plan the operational readiness of complex systems.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: Technological advances, changing needs and market dynamics generate a multitude of obsolescence issues in almost all sectors of activity. This represents serious challenges for companies, particularly in terms of quality, availability and maintainability. These challenges are particularly acute for complex systems with a long lifespan such as trains or planes, which must maintain acceptable levels of performance for many years. The obsolescence of components, documentation, tools and personnel skills are all industrial risks that must be controlled. In this context, this research presents models to represent the impact of obsolescence on the availability of complex systems. The objective is to show the mechanisms of these impacts. These models, built using Petri nets, then make it possible to estimate the possible degradation of availability following the occurrence of obsolescence, an aspect not addressed in this article. The modeling is based on a principle of classifying components into four classes, allowing to model the multi-level architecture of a system. By associating a set of Petri models with each class, and by synchronizing the models between them, the impact of obsolescence on availability is clearly described. The article ends with a number of conclusions and in particular with a presentation of the research carried out to predict the availability of systems in the presence of obsolescence of components, documentation, personnel or tools.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Complex adaptive systems and emergent synthesis in manufacturing, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Complex engineered systems (CES) consist of interconnected components with intricate interactions, making early-life failure diagnosis, particularly those stemming from manufacturing processes, highly challenging. Minor deviations in process parameters can affect components and subsystems, potentially leading to system-wide failures. Manufacturing deviations in individual components, though not necessarily flagged as defects, can combine in unexpected ways, causing broader system failures. Diverse fault pathways often produce similar failure symptoms, further complicating root-cause analysis. This paper introduces the Hierarchical Interaction and Fault Abstraction Framework (HIFAF) to represent the relationships among components, manufacturing processes, associated failure modes, and causal fault events for complex engineered systems. By integrating failure modes and fault events as intermediate nodes, the framework captures fault interactions with a level of abstraction, offering a structured approach to managing complexity. Realized as a knowledge graph, it provides a clear visualization of fault interaction pathways, supporting diagnostic processes. The framework is demonstrated through a case study on a single-stage gearbox system manufactured in-house, showcasing its ability to represent system-level failures and their links to minor manufacturing process deviations, revealing unexpected fault interactions.

Keywords: Supply chains and networks, Risk Management, Inventory control, production planning and scheduling
Abstract: The effectiveness of a production plan stands as a critical factor in a company's success and is underpinned by a resilient and robust supply chain network. A ubiquitous concern is the discrepancy observed when suppliers fail to deliver on time despite confirming orders well in advance. Such inconsistencies cast doubt on the reliability of individual suppliers and have cascading effects on the entire production line, often resulting in delays or unavailability of crucial items. This research tackles an in-depth exploration of the application of machine learning methodologies to extensive supplier-centric datasets sourced from an Original Equipment Manufacturer (OEM). At its core, the objective is to boost productivity and enhance supply chain robustness by proactively predicting supply issues much ahead of the scheduled production. Machine Learning models forecast supplier confirmations and expected deliveries and relocate orders to other suppliers, empowering the OEM to execute production plans. Continuous supplier interaction data enables the company to take timely corrective actions and assign resources efficiently, further improving supply chain productivity and resilience. By combining thorough data analysis with machine learning, organisations can visualise and predict to prevent potential supply chain setbacks. This approach sets the stage for a future where supply chain obstacles are not merely addressed in compliance but are proactively foreseen and prevented. Furthermore, predicting supplier behaviours in advance ensures smooth operations and meets market demands effectively. As a result, the company is positioned to realise greater profitability and efficiency, capturing opportunities related to customer satisfaction and market share expansion.

Keywords: Supply chains and networks, Smart transportation, Operations Research
Abstract: This study explores the benefits of integrating mobile depots into urban last-mile logistics. Although the deployment of zero-emission light vehicles, such as cargo bikes, has addressed key challenges, it has also created a need for more flexible warehousing. Mobile depots, i.e., vehicles functioning as dynamic storage hubs, offer this flexibility by supporting last-mile operations within designated urban districts. We propose a partitioning approach that combines a Mixed-Integer Linear Program (MILP), which optimises the scheduling of depot routes, with a set of routing subproblems that address local demand at each depot stop. Using a case study in Oxford, England, we demonstrate that mobile depots, when paired with our optimisation method, can significantly reduce total travel times. This improvement is largely attributed to the depot's mobility that enables vehicles to make shorter (re-)loading trips. These findings contribute to the broader discussion on mobile warehousing models and their potential applications in various sectors, including manufacturing and supply chain management.

Keywords: Supply chains and networks, Modelling Supply Chain Dynamics, Operations Research
Abstract: In this paper, we study the problem of optimally designing a sustainable crowdshipping system integrated into the public transport network of an urban area, where commuters transfer parcels between lockers at public stations. We propose a bi-objective mixed-integer programming model that maximizes customer satisfaction and the number of stations equipped with lockers (network extension), accounting for both fixed and variable costs. The mathematical model allows computing the structure of an optimal crowdshipping system across various scenarios, varying in network size, shape, and daily commuter numbers. The results gathered running tens of thousands of simulations provide valuable managerial insights for logistics service providers that evaluating the feasibility and benefits of investing in crowdshipping networks.

Keywords: Operations Research, Heuristic and Metaheuristics, Transportation Systems
Abstract: This paper introduces a novel variant of the Vehicle Routing Problem (VRP) that integrates relay points and crowdshipping to address last-mile delivery challenges. A metaheuristic approach based on Tabu Search is developed to optimize vehicle routing while considering constraints related to customer assignment to relay points and the allocation of crowdshippers for final deliveries. The proposed method was tested on a set of scenarios, and the results demonstrate its potential to enhance operational efficiency. These findings highlight the promise of the sharing economy in fostering sustainable and resilient last-mile delivery systems.

Keywords: Transportation Systems, Operations Research
Abstract: This research investigates a novel shuttle-based storage and retrieval system (SBS/RS) with proprietary lifts. In the studied system, the proprietary lifts are classified into two types: tote-lifts, which transfer totes from the storage tiers to the bottom tier, and shuttle lifts, which transport shuttles between tiers. We explore whether orders in an SBS/RS should be assigned to a busy shuttle on the same layer as the designated storage location or to an idle shuttle on a different layer. To address this problem, we develop a fork-join queueing network for the system and solve it using a matrix-based approximation method. Simulation experiments validate the accuracy of the analytical model. Experimental results demonstrate that: (1) There is an optimal probability of selecting a shuttle on the same layer that minimizes the system’s average throughput time. (2) As the arrival rate increases or the layer number in the system grows, the optimal probability of choosing a shuttle on the same layer increases.

Keywords: Operations Research, Optimisation Methods and Simulation Tools, Decision Support System
Abstract: This paper addresses a scheduling problem inherent to food-delivery operations, where a company managing a fleet of couriers must assign customer orders in a manner that minimizes a weighted function of delivery delays. Effective scheduling is critical to maintaining customer satisfaction and operational efficiency. We propose a novel deterministic algorithm that integrates mathematical modeling with a strategic courier repositioning approach. The primary objective is to assess whether incorporating strategic repositioning can enhance the Quality of Service (QoS) offered to customers by reducing delays and optimizing courier utilization. Our methodology is evaluated using real-world data from food delivery operations in the city of Rome, providing a practical context to measure its impact. The results highlight the potential benefits and limitations of strategic repositioning within this specific application, offering valuable insights for companies aiming to refine their delivery operations and improve customer experience.

Keywords: Supply Chain Management, Decision-support for human operators, Industry 4.0

Keywords: Supply chains and networks, Supply Chain Management

Keywords: Industry 4.0, Knowledge management in production
Abstract: Innovation and sustainability are essential to addressing global challenges, necessitating a robust collaboration between academia, industry, and government. This article explores how universities can integrate innovation competencies into engineering education to foster societal transformation. Using the Faculty of Engineering at NTNU as a case study, the paper examines the Innovation Pilot Program designed to enhance innovation culture, skills development, and practical actions for students, PhD students, researchers, and staff. The study employed interdisciplinary collaborations, workshops, and innovation competitions. The results indicate increased innovation awareness, higher engagement with industry, the creation of a sustainable innovation ecosystem and the need to develop structured courses in innovation to equip the students and the researcher with the skills needed to participate in societal transformation.

Keywords: engineering academic environment, innovation culture in academia, innovation competencies, STEM competencies, university-industry collaboration, Sustainable Development Goals

Keywords: Industry 4.0, Business Process Modeling
Abstract: Innovations in higher education have become critical for universities aiming to respond to societal needs and the rapidly evolving technological landscape. The purpose of this paper is to analyze how universities are adapting their strategies to foster an environment that promotes innovation and entrepreneurship while simultaneously enhancing their core educational missions. This examination draws on the experience of the Norwegian University of Science and Technology to illustrate these transitions, particularly within the Faculty of Engineering. Universities, traditionally centers for education, research, and societal service, find themselves under pressure to modernize and innovate in response to increased market competition and expectations from both public and private sectors. From 2021, discussions at the Faculty of Engineering led to a strategic orientation towards innovation, focusing on enhancing structures, tools, and cooperative functions that can strengthen university-industry-society collaboration. This paper will detail the methodologies employed to identify new directions, the outcomes of these initiatives, and their implications for future educational practices.

Keywords: innovation, HEI, strategy, sustainability, digital technologies

Keywords: Knowledge management in production, Quality management
Abstract: This paper presents the findings from an innovation pilot project initiated by the Department of Structural Engineering at the Norwegian University of Science and Technology, aimed at enhancing awareness and implementation of innovation in academic research. The primary purpose of the paper was to establish a framework that fosters innovation-related activities among the permanent scientific staff, emphasizing the transfer of research outcomes to industry and society. Utilizing a mixed-methods approach, the study assessed existing innovation activities, facilitated workshops, and conducted interviews with staff to gather insights on perceptions of innovation. The findings indicate a significant gap in the understanding of innovation among staff members, highlighting the need for more precise definitions and practical examples tailored to the engineering context. The implications of this research are manifold. First, the insights gained will guide the formulation of targeted training programs that equip staff and students with the skills necessary to navigate innovation processes effectively. Furthermore, the study emphasizes the importance of establishing robust collaborations with industries to enhance the applicability of academic research. This pilot serves as a blueprint for similar initiatives within the Faculty of Engineering and sets the stage for a more profound cultural shift towards innovation across Norwegian universities, ultimately contributing to the nation's goals of sustainability and competitiveness.

Keywords: Modelling Supply Chain Dynamics, Operations Research, Supply Chain Management
Abstract: The logistics sector plays a pivotal role in driving economic growth, enhancing productivity, and improving business returns. Existing studies have explored various aspects of these relationships, but despite these advancements, key gaps remain in understanding the nonlinear and dynamic relationships between productivity (measured in work hours), business growth, and returns, particularly in regional and sectoral contexts. While prior studies have examined individual components—such as service quality (Roslan et al., 2015), employee motivation (Lizbetinová et al., 2022), and green practices (Karaman et al., 2020)—there is limited integration of these dimensions into a cohesive analytical framework. Additionally, existing research often overlooks temporal dependencies and lagged effects of productivity on growth and returns, which are crucial for strategic planning. This study seeks to address these gaps by employing a robust empirical framework that combines dynamic regression analysis with residual diagnostics, aiming to uncover the nuanced relationships among productivity, growth, and returns within the logistics sector of Estonia. By addressing this gap, the research provides actionable insights for policymakers and businesses to optimize logistics performance and align sectoral growth with sustainability and profitability objectives. This study examines the dynamic interplay between productivity, business growth, and return on investment (ROI) in Estonia’s logistics sector from 2006 to 2020. Employing advanced regression and correlation analyses on panel data, the research identifies key relationships among variables such as productivity in work hours, growth, and returns across sub-sectors, including postal services, warehousing, and rail transport. Residual diagnostics and robust regression methodologies validate the statistical significance of the models. Graphical representations, including correlation matrices, forecasted trends, and residual analyses, provide a comprehensive visualization of the study’s findings, offering actionable insights for optimizing sectoral performance.