Keywords: Supply Chain Coordination, Optimization and Control, Engineering Applications of Artificial Intelligence
Abstract: With the prevalence of lean and just-in-time principles, traditional supply chains often exhibit inflexibility, leading to challenges in satisfying extensive customized orders and managing risks during disruptions. Thus, there is a need for a more flexible, resilient, and collaborative network and strategies to tackle the aforementioned challenges. In this study, we introduce a new supply network called the industry supply chain, aimed at enabling collaborative decision-making and dynamic reconfiguration. We create a graph neural network model to promptly identify sudden disturbances and devise a distributed multidisciplinary optimization model to facilitate collaborative reconfiguration. The experimental findings from an air-conditioning industry supply chain show that network reconfiguration under real-time disturbance detection reduces losses and improves operational stability.

Keywords: Supply Chain Management (SCM), Inventory Control and Dynamic Pricing, Physical Internet
Abstract: We consider a supply network for medical implants in the healthcare system, where the vendor manages multi-hospital warehouses under a vendor managed inventory (VMI) policy and uses RFID technology to reduce the network’s total costs and enhance customer service. The vendor needs to determine the location of the warehouses, the inventory policy, and the RFID investment so that the total costs are minimal. In this research, we develop a mixed-integer non-linear program model to formulate the integrated decision problem that combines the location-inventory problem with the allocation assignment of RFID technology and its investment level. A real-world problem is resolved and analyzed with the proposed approach.

Keywords: Information Sharing, Computational Science
Abstract: The article gives the problem of resource assignment and performance optimisation in intelligent supply chain (ISC). The task of the resource assignment for intelligent supply chain is multi-product and multi-criterion task. Scale of the ISC determines the allocation of the suppliers and consumers of product. The customer demands for certain kind of product in ISC can be deemed as stochastic process. As goal of performance optimization task we should to evaluate parameters of ISC through assignment of production resources. The article presents the resource assignment model of ISC and its performance measures. On the base of simulation the verification and validation of presented model is carried out. The problem statement, solution method, numerical example and validation of model via simulation are represented for resource assignment and performance optimization model.

Keywords: Process Planning/Equipment Selection, Diagnostic and Optimization, Manufacturing Cells
Abstract: This work proposes a highly parallelized and efficient optimization procedure for finding the globally optimal robot base placement in industrial assembly tasks. This procedure fully embeds a point-to-point (P2P) trajectory planner to evaluate the placements, considering kinodynamic robot limits, collisions, robot configuration changes, and a cyclic constraint. The investigated optimization goals are cycle-time optimality, time-energy optimality, and adaptivity to dynamically changing throughput. The latter uses an optimization-based method to scale the cycle-time-optimal trajectory in low-throughput situations to reduce energy consumption. The approach is used to optimize the base placement in two robotic work cells, showing a reduction in the cycle time of up to 44% and a reduction of energy consumption of up to 53% in a best-to-worst comparison.

Keywords: Line Design and Balancing, Stochastic Processes, Monte Carlo Methods
Abstract: In response to the growing emphasis on recycling and reusing end-of-life products, the effective management of disassembly processes under uncertainty has become a significant area of interest for researchers and practitioners alike. This paper offers an initial comprehensive review of the literature concerning the disassembly line balancing under uncertainty. The problem of stochastic line balancing is defined as the assignment of disassembly tasks to workstations in an uncertain environment while achieving specific objectives. Our review examines the diverse uncertainties discussed in the literature. We analyze the modeling techniques, approaches, and objectives associated with each type of uncertainty. Drawing from insights gained through the review of 54 publications, we subsequently engage in a discussion and outline potential directions for future research.

Keywords: Engineering Applications of Artificial Intelligence, Capacity and Performance Evaluation, Self-learning Models
Abstract: Industrial companies face challenges caused by volatile business environments. In particular, changing customer demands can have large impacts on their performance. Medium-term capacity management addresses this challenge by adapting the production capacity to the customer's demand. This is usually done by experts or, in the best cases, supported by production planning software using linear programming or genetic algorithms. There is a growing trend for using production simulations or digital twins. These simulations can be coupled with demand forecasts and machine learning to create a planning assistant tool. This tool can support the operations manager to find the right set of measures to adapt the production capacity based on changing customer demand. In our work, we design such a planning assistant tool by using different algorithms, including deep reinforcement learning. We apply it in a learning factory for performance comparisons and can show promising results of deep reinforcement learning. Furthermore, we can show that this simple decision support system outperforms humans in the test setting.

Keywords: Engineering Applications of Artificial Intelligence, Real-time Artificial Intelligence, Self-learning Models
Abstract: The field of intralogistics is ideal for applying artificial intelligence (AI). However, there is currently no comprehensive framework for AI-enabled intralogistics that considers decision making layers. This paper aims to fill that gap by providing context, reviewing recent publications, and identifying key elements for framework development. It explores how AI can be used in intralogistics system design, planning and operations, at both physical and virtual levels. Our focus is on engineering pragmatic systems within the intralogistics domain, with a framework comprising human interaction, intelligent agents, and devices. The paper also addresses training data for AI-enabled intralogistics.

Keywords: Quality Predictive Monitoring, Intelligent Predictive Control, Engineering Applications of Artificial Intelligence
Abstract: This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques. The key contributions include developing ML models that accurately predict various roughness values and identifying redundant sensors, particularly those for measuring normal cutting force. Removing these sensors can reduce costs without compromising predictive accuracy, demonstrating the potential of explainable ML to enhance cost-effectiveness in machining.

Keywords: Optimization and Control, Intelligent Manufacturing Systems
Abstract: In manufacturing dynamic scheduling is a complex task which is influenced by various factors, including several possibly contrary optimization objectives. While multi-objective optimization approaches are not novel anymore, prevailing deep learning solutions lack in the ability to dynamically adjust the preferences of the different objectives. For this reason, we developed a collaborate human-AI scheduling algorithm where Reinforcement Learning (RL) is utilized for the optimization of single objectives, while the human is left in control to flexibly mix and weight the influence of each objective into the final schedule. The approach performs on par with prevailing RL multi-objective approaches. However, it surpasses the later in its ability to dynamically adjust the preferences of the objectives without retraining.

Keywords: Static and Dynamic Facility Layout, Reconfigurable Manufacturing Systems (RMS), Engineering Applications of Artificial Intelligence
Abstract: Facilities Layout Problems (FLPs) aim to efficiently allocate facilities within a given space, considering various constraints such as minimizing transportation distances. These problems are commonly encountered in various types of advanced manufacturing systems, including Reconfigurable Manufacturing Systems (RMSs). RMSs enable easier layout changes to accommodate shifts in product mix, production volume, or process requirements thanks to their modularity and changeability. Reinforcement Learning (RL) has proven its efficiency in addressing decision-making problems. Therefore, this paper introduces a comparative study between two RL algorithms to solve FLPs: Advantage Actor-Critic (A2C) and Q-learning algorithms.

Keywords: Ergonomic Aspects of Industrial Technologies, Cognitive Aspects of Automation, Human-Automation Integration
Abstract: In Industry 5.0, worker well-being is paramount for organizational resilience and sustainability. Physical fatigue, work-life balance, and job competency significantly impact worker welfare and, therefore, efficiency and effectiveness. This study collects data in different industrial scenarios using non-invasive wearable devices for dynamic data and questionnaires for quasi-static data. Using Machine Learning algorithms, including Random Forest and Feedforward Neural Network models, the study predicts the physical fatigue of workers across multi-class and binary classifications. The developed Fatigue Monitoring System software integrates these models to monitor fatigue and improve worker well-being.

Keywords: Mathematical Approaches for Scheduling, Human Resource Allocation, Performance Evaluation
Abstract: This study investigates human-centered task allocation, focusing on factors such as cognitive load, physical demand, and ergonomics. A discrete event simulator was developed to validate the task allocation results of the Q-learning optimization. The simulation evaluates the process and resulting task allocation plan based on predefined human-centered objectives. Through a case study on remanufacturing, we demonstrate how to optimize the coordination of a robotic arm and two human operators to reduce overall workforce requirements. The simulation allows for the analysis of operators’ cognitive and physical workloads over time, enabling exploration of not only typical time balances but also cognitive and physical burdens.

Keywords: Human-Automation Integration, Ergonomic Aspects of Industrial Technologies, Cognitive Aspects of Automation
Abstract: Human-Machine Interfaces are pivotal in modern manufacturing, serving as the critical link between human operators and complex machinery. This paper explores the evolution of HMIs, emphasizing their importance in enhancing efficiency, safety, and productivity within manufacturing environments. While HMIs offer real-time monitoring and control functionalities, their effectiveness hinges on human-centric design principles. Incorporating insights from human-centred design, this research presents a case study conducted at the SLIM laboratory of the University of Bergamo. The study encompasses theoretical exploration, HMI re-design, and user evaluation within an automatic assembly line setting. The research identifies key factors influencing HMI usability through critical analysis and user feedback and offers valuable insights for optimizing HMIs and empowering operators in the digital manufacturing landscape.

Keywords: Human-Automation Integration, Real-time Evaluation of Fabrication, Machining and Assembly Systems
Abstract: Due to the growing presence of digital technologies in factories, an increasing number of automated processes systematically record various data such as timestamps, product IDs, quantities, and defects. However, this is still not the case for many manual assembly lines, which often rely on manual data acquisition where workers, in addition to the assembly tasks, are asked to record data manually. Such manual data generation can significantly reduce the data quality and increase the variation of the data. In this paper, the authors study this problem by conducting a case study of a manufacturer of engine components based in Switzerland. The paper examines the antecedents of deviations in manually collected assembly line data—both from a technical and behavioral perspective. Based on the findings, a model is developed for the case company that compares manually collected data to the same data in the planning systems to identify and act on data discrepancies. The paper concludes with practical guidelines on how to mitigate data quality loss in manual assembly.

Keywords: Human-Automation Integration, Manufacturing System Engineering, New Product Development
Abstract: We conducted this study to investigate the rationales and controversies regarding the shift from Industry 4.0 to Industry 5.0. We employed the systematic literature review methodology to analyze the current rationales and conflicts on this specific topic. To support our research questions, we first examined 5 review articles and then carried out a review analysis of 23 articles that we included through the review methodology. Finally, we proposed a conceptual framework that outlines the digital transformation of generic organizations through the integration of Industry 4.0. In this transformation, we identified areas that require further discussion to reach a common consensus on the term “Industry 5.0”.

Keywords: Reconfigurable Manufacturing Systems (RMS), Line Design and Balancing, Sustainable Manufacturing
Abstract: One of the most topical issues of sustainable development is extending the life cycle of production systems. Reconfigurable manufacturing system (RMS) aims to adapt efficiently to the rapidly changing market. One lever to extend the life cycle of RMS is the ability to quickly adapt to a new product (called convertibility). In this article, we present a model for the design of RMS, optimizing both its convertibility to prolongate its life cycle, and its cost to remain competitive. The convertibility is assessed on three axes: the choice of machines, the choice of transportation systems, and the physical configuration of the machines in the plant and their connection. We formalize this problem and model it by a bi-objective mixed-integer linear program. We illustrate our approach on an example.

Keywords: Line Design and Balancing, Reconfigurable Manufacturing Systems (RMS), New Product Development
Abstract: Assembly lines typically operate for several decades. Process engineers reconfigure the lines several times during the product family's life cycle, whereas product families may change several times a year in response to sales and marketing demands. These reconfigurations are often expensive and inefficient if the line is not flexible enough. The current study explores the feasibility of creating a line that takes product evolution into account during the line's life cycle. We study a line where a worker/robot and equipment pieces required are located at each station. When a new product model replaces one of the current variants in the product family, the line reconfigures to produce different product models from the same family. Reconfiguration can re-assign some tasks and rearrange equipment and resource elements. We formulate a model that accounts for the uncertainty of the product family evolution and the market demand. We propose an adversarial approach for the robust optimization of the mixed-model assembly line design for the worst-case scenario from a scenario tree for the future product family requirements. We run computational experiments using benchmark data. The results demonstrate that the developed adversarial approach outperforms the classical methods from the literature in terms of CPU time and solution quality. Copyright © 2024 IFAC

Keywords: Uncertainty Modelling, Reconfigurable Manufacturing Systems (RMS), Decision Support System
Abstract: Traditional manufacturing paradigms cannot deal with the current pace of uncertain events in demand, supply and beyond. Reconfigurable Manufacturing Systems (RMS) are designed to adapt to these challenges in a rapid and cost-effective way. In order to decide when and how to reconfigure an RMS, it is necessary to identify the external events which trigger change in the system. This paper proposes a framework for uncertainty representation in RMS based on three levels of uncertainty and decision horizons. An illustrative example shows how such framework can be used by researchers and practitioners to better understand RMS and its context.

Keywords: Reconfigurable Manufacturing Systems (RMS), Process Planning/Equipment Selection, Sustainable Manufacturing
Abstract: This research examines the integration of workforce and process planning in reconfigurable manufacturing environment, focusing on sustainability’s economic, social, and environmental dimensions. It evaluates social sustainability through new indicators, including flexible working hours and workforce hazard risks. A new mixed-integer linear programming model is proposed to minimize costs, social sustainability metric, production time, and hazardous waste. The model’s effectiveness is assessed with RMS benchmarks using the augmented weighted Tchebycheff method for multi-objective optimization and data envelopment analysis to rank solutions, providing a detailed evaluation of RMS configurations.

Keywords: Reconfigurable Manufacturing Systems (RMS), Optimization and Control, Dynamic Systems
Abstract: Scalability is crucial for reconfigurable manufacturing systems (RMS), allowing throughput capacity adjustments based on market demand shifts. This research applies control theory to assess scalability's impact on RMS, understanding dynamic responses to production demand variations. Strategies are explored to efficiently manage changes, maximizing manufacturing process effectiveness. Using a discrete transfer function, the study employs virtual reference feedback tuning (VRFT) with a proportional-integral-derivative (PID) control strategy. This sophisticated, data-driven approach analyzes system responses, identifying opportunities for optimization. Integrating VRFT-PID offers a nuanced perspective, enhancing understanding of RMS behavior and refining control strategies to optimize throughput capacity in response to market dynamics.

Keywords: Information Sharing, PLM Systems, Sustainable Manufacturing
Abstract: Information traceability is vital in the current manufacturing era. Therefore, this paper explores the various benefits that Digital Product Passports DPPs bring to a value chain, from manufacturing to recycling at the end of life. We study the incremental improvements in manufacturing transparency, supply chain efficiency, and the remanufacturing process enabled by DPPs through a thorough analysis of real-world examples and industry scenarios. We also look into how DPPs improve ownership satisfaction and the sales experience by providing dealers, distributors, and car owners with thorough product knowledge. The paper also emphasizes how DPPs simplify the recycling and remanufacturing processes, highlighting how they help preserve materials and lessen environmental impact. To highlight the critical role that digitalization plays in accomplishing sustainable development goals and advancing the circular economy, this paper describes how DPPs are implemented and impact the automotive industry. An example case from the automotive case is presented and the DPP benefits to this sector are identified. The results provide guidance for the automotive industry's shift to a future that is more transparent, efficient, and environmentally conscious. These insights may encourage wider adoption in other industries.

Keywords: Optimization and Control, Modelling Supply Chain Dynamics, Sustainable Suppy Chain
Abstract: The study proposes an optimization model to mitigate greenhouse gas (GHG) emissions when trucks are idling at container terminal gatehouses. A queuing model application for tandem queues and a bi-objective optimization related to transport problems have been used in this study. Data collection is conducted at a leading container terminal at one of the container terminals at a hub port in the Asian region. It is not difficult to predict ITT arrivals through the initial analysis of truck arrival patterns when compared to the imports and exports truck arrivals. It is observed that unnecessary fuel consumption and GHG emissions can be reduced by up to 42% compared to the given conditions. The presented solution reduces the major amount of problematic GHG emissions while the model can be employed by adjusting to given conditions. Similarly, the model can be expanded based on the factors such as the number of operators and equipment in the loading/unloading processes, shortage of chassis for transport between quay-yard, and scarcity of container slots in the yard or warehouse to the optimization of truck arrivals can be proposed as a future study area from this study.

Keywords: Supply Chain Management (SCM), Modelling Supply Chain Dynamics, Uncertainty Modelling
Abstract: The the LARG paradigm involves and combines different perspectives: Lean, Agile, Green, and Resilience. These four perspectives are recognized as a suitable response to the strong demand fluctuations and characteristics of modern variables and unpredictable markets. Their integration into modern supply chains is also crucial from a strategic point of view. This study develops a simulation model useful for measuring and evaluating the LARG performance of a supply chain operating in the fast-food industry. Different configurations have been tested varying the number of players and the target service level. Then, different scenarios with distortions have been compared. Results show that the service level influences the LARG performance regardless of the kind of disruption. The supply chain structure can affect LARG performance when the demand increases, while it seems not relevant if the demand decreases.

Keywords: Integer Linear Programming, Energy Efficiency, Mathematical Approaches for Scheduling
Abstract: In response to climate change, industries strive to curtail energy consumption while maintaining production efficiency. Focused on time-dependent electricity prices, this paper addresses the scheduling challenge in a flexible flow shop machine environment. The goal is to minimize both makespan and total electricity costs in a grid-connected manufacturing setup with battery storage, solar power, and a strategic turn on/turn off machine approach. A multi-objective mixed-integer linear programming model is developed, employing the epsilon-constraint method to reveal the trade-off between objectives and present Pareto-front solutions. The ensuing experimental study allows for the validation of the model and identification of its limits.

Keywords: Condition-Based Maintenance, Diagnostic Systems, In-process Manufacturing Monitoring
Abstract: Machine data plays a significant role in a production environment, as operators strive to constantly optimize their processes and increase efficiency while maintaining the required quality parameters. Recorded machine data provides comprehensive insights into the condition of the monitored machines and their components. This data can be used, for example, in operations for condition monitoring, predictive maintenance, quality control and automation. However, the collection, transfer and storage of machine data is still a key challenge due to various causes. Current condition monitoring approaches are often used alongside other data-intensive methods such as machine learning and deep learning algorithms. However, there is a pressing need to develop new concepts for discrete data analysis that enable accurate fault diagnosis and effective maintenance planning without relying on extensive datasets. This paper presents a discrete concept for resource efficient production system condition monitoring. This concept consists of a data processing pipeline where many data reduction steps are done. Using the collected data of a drive, a principal component in production, we showed that the reduction of the parameters to be collected and the aggregation of data does not necessarily change the informative value and consequently condition/state determination is still possible. The data reduction steps along a data process pipeline showed the potential to save resources. This should relieve the network and reduce storage and computing capacity.

Keywords: Intelligent Manufacturing Systems, Predictive Maintenance
Abstract: Among the Industry 4.0 technologies transforming manufacturing environments, the Digital Twin (DT) technology is gaining attention as a driver for improving manufacturing performance and a platform to enhance collaboration. While the advantages of leveraging DT for supporting decision-making in production planning and maintenance planning are recognized, research taking an integrated perspective is scarce. In addition, besides the growing trend, recent studies highlight the limited adoption of DTs in process industries, emphasizing the necessity to address the unique features of their production processes. Through a literature review, this paper points out the main obstacles encountered during the implementation of DT in process manufacturing industries, as well as shows the research gap in exploiting DT to support the integration of decisions concerning production and maintenance and underscores the need for further investigation in this direction to discover its full potential.

Keywords: Manufacturing System Engineering, Manufacturing Execution Systems
Abstract: In addition to inheriting concepts and methodological principles from System Engineering, Model-Based Systems Engineering (MBSE) emphasizes the role of modelling and models in engineering processes. Different types of models are then constructed and used, each being a digital representation of a system that is undeniably partial, but also necessary. It highlights and enables the analysis of certain characteristics of this system, e.g. its behaviour, its functions or its composition to make relevant design choices. Digital Twins are the ultimate representation of a system, expected to be faithful and realistic for the purpose they serve, as well as to be connected and synchronized to the modelled physical asset. Hence, they support stakeholders to manage, train or optimize the target system when implemented and put into operation. However, defining and engineering Digital Twins remains a difficult process, with little methodology available to build and ensure it remains faithful to a continuously evolving reality. MBSE provides concepts and a methodological framework that are well adapted to solve complex engineering problems with many constraints. This position paper introduces a MBSE based Digital Twin engineering framework foundation, applied to an manufacturing system.

Keywords: Flexible and Reconfigurable Manufacturing Systems, Flexible Manufacturing Systems (FMS), Optimization and Control
Abstract: Due to the fact that uncertainties and disruptions are increasing because of the complexity of the supply chains, resilience has become a very important and imperative aspect for modern manufacturing industries. The ability to overcome challenging circumstances without significant additional costs is referred to as resilience. Digital technologies are considered as key enablers for manufacturers to achieve resilience in their manufacturing systems either in shopfloor or in value chain level. This work presents the framework of an information system for implementing Resilient Manufacturing value chains. The objective is to achieve resilience by applying the framework which leverages on data space technology, such as Gaia-X or Industrial Data Space (IDS), and the Asset Administration Shell (AAS), where AAS supports the implementation of Digital Twins. Moreover, this work discusses the integration, within the proposed architecture, of two services namely Resilience Assessment and Reconfiguration Services. The architecture is validated in an industrial production environment from the steel industry. Finally, a discussion on existing challenges, limitations and future potentials takes place.

Keywords: Manufacturing System Engineering, Intelligent Manufacturing Systems, Integration of Knowledge/Competence in Enterprise Modelling Framework
Abstract: The development of flexible and highly efficient production systems is advancing through the use of digital twins and artificial intelligence. However, these technologies mainly address predefined problems, leaving humans as the sole entities capable of innovatively solving undefined challenges. This has led to the advocacy for human-centered Cyber-Physical Systems (CPS), which place humans at the core of decision-making, supported by these technologies. The Digital Triplet framework aids this by focusing on engineers using digital twins for problem-solving. This paper discusses the effectiveness and challenges of the Digital Triplet, as revealed through its application to multiple industrial cases.

Keywords: Manufacturing System Engineering, Flexible and Reconfigurable Manufacturing Systems, Predictive Maintenance
Abstract: Digital twin (DT) is an important technology to support the realization of the digital transformation, connecting the physical asset to its virtual copy and fostering real-time monitoring, simulation, and decision-support. However, the benefits of DT depend on its intended purpose and application, which is impacted by the design structure configuration that is used for its implementation. This paper discusses the advantages and challenges of considering different organizational structures for the implementation of DTs, namely centralised, hierarchical and decentralised, complemented with a case study that was used to analyse the implementation of DT focusing on centralised and decentralised approaches. Additionally, the paper includes an analysis of the main aspects of DT structures and their design guidelines, the key enabling technologies and the main challenges of distributing DTs.

Keywords: Ergonomic Aspects of Industrial Technologies, Warehouse Management Systems, Human Resource Allocation
Abstract: Order picking is a critical and resource-intensive process within warehouse operations. Many warehouses still heavily rely on human operators for picking orders. However, human order pickers have become a scarce resource, partly due to the physically demanding nature of the order picking process. If warehouses want to remain competitive, they should properly account for physical worker well-being. Unlike earlier research which mainly focused on objective metrics, this paper investigates the factors that contribute to subjective physical exertion. Specifically, we conduct a large-scale lab experiment (N=164) and derive the perceived ergonomic exertion with the Borg CR-10 scale. Results show the significance of shelf height, product weight, and product quantity on ergonomics. On the other hand, elements such as picker sociodemographics, the prevailing incentive system, or the degree of autonomy are less suitable to predict perceived physical exertion; they might be more relevant to influence psychosocial well-being though. Our study shows thus that physical worker well-being is primarily determined by the attributes of the order to be picked. Based on our results we propose several suggestions for future research and ergonomic OP system design.

Keywords: Cognitive Aspects of Automation, Human-Automation Integration, Integration of Knowledge/Competence in Enterprise Modelling Framework
Abstract: This paper aims to reduce potential human errors and loads that arise from the lack of novice human operators and different interfaces in advanced machine tool industries. A digital assistant using generative artificial intelligence is designed to answer questions about machine terminologies, and operation sequences happening in an alarm state. Combining Fine-Tuning, Retrieval Augmented Generation, and Prompt Engineering, it solves problems of common purpose large language models. The proposed system is implemented on a local server and connected to a mobile device. It shows increasing quantitative accuracy from 51% to 79% and 84% in the fine-tuned and retriever model, and the qualitative score increases from 21 to 25 in the retriever model.

Keywords: Human Resource Allocation, Capacity and Performance Evaluation, Flexible and Reconfigurable Manufacturing Systems
Abstract: Industries recently have faced high uncertain market demand with a greater product variety and a shorter life cycle. The primary aim of assembly systems is to increase line efficiency by maximizing the ratio of throughput to incurred costs while maintaining high flexibility. Workers are essential in determining line performance since most assembly tasks are still manual. Consequently, worker diversity plays a critical role in the operation of an assembly line, not only in terms of efficiency and productivity, but also from a human perspective. The human factor has a significant impact on task execution times and the overall production process; in any event, few studies considered human beings in assembly line operations. This study aims to explore the effect of human beings on assembly line performance; the case of an Italian company in which workers’ behavior deeply influences production performance is presented, with the aim of analyzing different alternatives for the planning strategy in order to meet this requirement. The studied assembly line consists of three manual workstations and two automated workstations. It operates in a multi-model production environment and has an imbalance level of 50%. The proposal is to manage the allocation of workers through job rotation scheduling to ensure equal treatment despite different levels of efficiency. The study outlines three scheduling alternatives, highlighting the advantages and disadvantages of each.

Keywords: Robotic Systems, Human-Automation Integration, Modelling for Cooperative Transportation Systems
Abstract: Thanks to rapid technological developments in robotics, various automation technologies have been introduced into different logistics systems. A human–robot collaborative logistics system can contain different technologies. Until now, most studies have focused on technologies that could only partially automate the logistics process. In contrast, some market-ready products exist with which the entire logistics process could be automated. An example is the hybrid order picking system, which utilizes autonomous mobile robots to conduct picking and transporting tasks and can be integrated into manual warehouses. In this case, human workers and robots can be modeled as heterogeneous agents with strengths and weaknesses. To date, the issue of how to model these logistics systems has remained largely unaddressed. Inspired by this system, this work intends to contribute to the discussion of possible (analytical) modeling approaches to leverage the strengths of both humans and robots to achieve better outcomes. To this end, a systematic literature review was conducted to find usable approaches in adjacent disciplines. We identified modelling approaches in the field of last-mile delivery, in-house transportation, assembly, and robotics, which considered a (human-robot) heterogenous team in the task allocation. Efforts are then made to construct a framework for possible design variables for such logistics systems to offer promising insights and perspectives for future research.

Keywords: Mathematical Approaches for Scheduling, Human Resource Allocation, Ergonomic Aspects of Industrial Technologies
Abstract: With their variety of skills and competencies, humans are an important resource in logistics. To prevent workload-related diseases, improve work design, and maintain productivity in manual processes as order picking, an optimization approach to integrate ergonomics into resource planning is introduced. The objective is to distribute physical workload among workers in a shift evenly. Applying the Design Science Research Method, this paper presents the conceptual process design of the developed ergonomic order assignment. An assessment of the individual workload per order and worker combination is introduced, considering individual characteristics of typical orders. It is followed by a reference implementation.

Keywords: Closed-loop Supply Chains, Sustainable Manufacturing
Abstract: The prevailing linear economic structure and our complex global supply chains are reaching their tangible limits, as evidenced by the impact of current crises such as energy scarcity. A shift from a linear to a Circular Economy is necessary to meet the urgent environmental and economic challenges. This paper aims to address a significant gap in the literature by utilizing a structured literature review and the Supply Chain Operation Reference (SCOR) model. Rather than focusing on specific contexts or industries, this paper aims to examine the intercorrelation and interdependencies between the barriers from a supply chain perspective. Therefore, the diverse conceptual background of the Circular Economy and the complexity of its implementation due to the interconnectedness of barriers that span regulatory, cultural, market, and technological aspects are analyzed. The interdependence of barriers in the different supply chain stages is highlighted in a qualitative and explorative research approach. The key findings reveal that a successful supply chain management strategy that integrates Circular Economy principles along the whole supply chain is crucial for successfully transitioning from a linear to a Circular Economy. We identify necessary lines of future research to facilitate the successful transition and overcome the current barriers along the supply chain.

Keywords: Multi-Agent Systems and Heterarchical Topology for Manufacturing, Decision Support System, Multi-agent Simulation
Abstract: The increasing need of product individualization and rapid demand fluctuation leads industrial manufacturers to leveraging new control solutions for production planning and control. Among these, distributed control approaches for the multi job shop scheduling problem could be a promising solution to make smart manufacturing systems more responsive to the market. In this context, this paper aims at investigating the effectiveness and the benefits of distributed control approaches for solving the multi job shop assignment problem on a real-world smart manufacturing process involving the production of high-vacuum solar panels. The validation is carried out by leveraging Cyber-Physical Systems modelling and a digital model-based procedure which, interacting with the physical plant, provides the proper job assignment in order to optimize the overall makespan and work-in-progress in a fully-distributed fashion.

Keywords: Process Planning/Equipment Selection, Data Models, Ontology for Enterprise Interoperability
Abstract: A standardised description of machines and equipment as well as their behaviour is essential for the planning and operation of heterogeneous production environments. The article introduces standardisation approaches of Equipment Behaviour Catalogues from the ISO 16400 series as well as other concepts for standardised asset and equipment description in the sense of the digital twin. In this context, an information model for the production life cycle is introduced to harmonise the required information and interfaces for their exchange in a manufacturing organisational context.

Keywords: Sustainable Suppy Chain, Information Sharing, Sustainable Manufacturing
Abstract: The wood industry faces challenges from multiple angles, markedly from rapidly changing market environments, policy and compliance demands, and, in general, the digitalisation of its core processes. A particularly challenging area is the transition to digitalised, cross-domain supply chain management. Thus, this paper addresses the furniture industry's transition from analogue to Digital Product Passports (DPPs) following the EC's initiative to promote circularity, foster innovative business models, and assist in sustainable product management. This paper provides a blueprint DPP for the furniture industry that explores not only what requirements can be expected from the upcoming regulations but also how existing data management practices can be adapted to meet these requirements.

Keywords: Information Sharing, Sustainable Suppy Chain, Closed-loop Supply Chains
Abstract: Circular economy strategies, driven by national and EU regulations and market demand, are reshaping the manufacturing industry towards sustainability and resilience. Closed-loop supply chains, integrating secondary materials, aim to minimize waste and carbon footprint while enhancing supply chain flexibility. Leveraging online B2B marketplaces, manufacturers, suppliers, and other stakeholders engage in dynamic networks, optimizing resource utilization through competitive service offerings. This work explores the integration of stakeholders across the product lifecycle into a digital marketplace with secure data exchange, following IDSA and Gaia-X concepts. It highlights the Digital Product Passport's (DPP) role in sharing product data throughout the product lifecycle. DPP and service offerings are realized using the Asset Administration Shell. Implemented in the SmartFactory^KL demonstration environment, it showcases circular process adoption and carbon footprint reduction through a service-oriented marketplace.

Keywords: Capacity and Performance Evaluation, Human-Automation Integration
Abstract: Human-robot collaboration promises to free the human to multitask and engage in cognitive work while the robots assists with physical tasks, therefore increasing productivity. However, this collaborative paradigm requires continuous attention from human operators, which could potentially strain their cognitive resources. Excessive attention demands can lead to safety hazards, increased errors, and reduced efficiency. Despite its critical importance, there is limited empirical research on attentional factors in industrial human-robot collaboration. In this study, we explore attentional multitasking in collaborative human-robot assembly settings. Our experimental setup involves participants performing a wire harnessing task with a collaborative robot while simultaneously completing a Go/No-Go test as a secondary task. To observe the effect of multitasking, we varied the difficulty of the secondary task across two levels and analysed its impacts on work performance and workload. Our results confirm threaded cognition theory, suggesting that human-robot collaboration could reduce cognitive capacity by depleting attentional resources, leading to higher errors and cycle times during multitasking. This underscores the importance of a detailed understanding of attentional factors in human-robot collaboration. We discuss our findings and their implications, and provide insights into the adjustment and design of human-robot collaboration tasks in the industry.

Keywords: Human-Automation Integration, Ergonomic Aspects of Industrial Technologies, Cognitive Aspects of Automation
Abstract: Human-robot interaction (HRI) is one of the most promising technologies in academic, social, and industrial contexts. Many industries consider HRI as an opportunity to combine the precision, repeatability, and speed of robotic automation with the human’s flexibility, ability, and inclination to problem-solving. Nevertheless, human-centred factors and their impact on performance are still underestimated in this research area resulting in an important research and application gap. This paper addresses this gap by developing a conceptual framework that identifies and combines several key elements from the human-centred and system-centred design paradigms. The aim is to support the design and implementation of human-centred working environments that consider human roles and well-being while maintaining high production performances. This can improve the digital transition to Industry 5.0 enabling the combination of two visions and avoiding the risk of implementing HRI without attention to human factors. This study will contribute to assist practitioners and researchers in identifying the relevant elements to evaluate performance in HRI systems.

Keywords: Integration of Knowledge/Competence in Enterprise Modelling Framework, Data Models, Manufacturing Resources and Processes
Abstract: In today's evolving industrial landscape, enhancing workforce resilience by improving the ability to navigate complex and unforeseen scenarios, is essential. This paper explores the role of training, especially during disruptions, introducing an innovative approach that combines Building Information Modeling (BIM) and Virtual Reality (VR). This aims to ease the creation of automatic BIM-enriched VR training environments. However, this process revealed some technical challenges that need to be addressed for an effective implementation. Therefore, this paper covers the theoretical background of VR and BIM, their common applications, and develops the conceptual basis of BIM-enriched VR models. A prototype of this integration has been developed, based on the case study proposed. This allowed to highlight the challenges the paradigm entails and for the definition of suggestions to overcome them.

Keywords: Integer Linear Programming, Mathematical Approaches for Scheduling, Decision Support System
Abstract: The Patient Admission Scheduling (PAS) process involves efficiently managing the admission of patients to specific beds within relevant departments while addressing all their medical needs over a defined time horizon. This study delves into PAS within hospital network, emphasizing the collaborative nature of their interactions.Collaborative interactions are a common challenge in hospitals, where they need to collaborate and share resources to allocate patients to a limited number of beds within a specified timeframe, ensuring all necessary medical conditions are met. To address this challenge, a mixed-integer mathematical programming model for the PAS problem within hospital network is proposed with the aim of minimizing a weighted sum of unsatisfied constraints.

Keywords: Intelligent Manufacturing Systems, Ergonomic Aspects of Industrial Technologies, Embedded Devices
Abstract: The current shift in European demographic pyramids and the increase in the retirement age are posing serious challenges to the manufacturing sector. To address these social challenges, the recent Industry 5.0 paradigm is promoting human-centric value creation to achieve resilient and socially sustainable industrial environments. In this evolving scenario, this work conceptualizes the Safe Operator 5.0 digital architecture to safeguard the system and physical resilience of human-centric manufacturing environments leveraging three main layers. While workers are digitized through positioning, proximity, and physiological sensors, a machine learning-based cyber layer processes these data streams as well as managerial data to evaluate the efficiency and physical well-being of the workforce. Finally, a central management system provides feedback and early warning to final users.

Keywords: Sustainable Manufacturing, Discrete-event Simulation, Ergonomic Aspects of Industrial Technologies
Abstract: The ongoing environmental crisis motivates some businesses to shift towards a circular economy. This shift necessitates a comprehensive overhaul of their existing manufacturing processes to enhance their adaptability to circular principles. Amidst substantial uncertainty and a strong need for skilled human labour to handle intricate and variable tasks, operator training emerges as a pivotal element in guaranteeing the efficiency of the production system. Several solutions could be explored to improve operators' skills while integrating a strong social and environmental dimension. In this context, the use of simulation can carry out tests to optimize the acquisition of skills in circular manufacturing systems. However, the current simulation models rarely integrate knowledge from the human and social sciences to model operators in systems while they are crucial to the performance of circular workshops. This paper proposes a methodology to develop a digital simulation taking into account operators' health and skills acquisition in those systems.

Keywords: Sustainable Manufacturing, Modelling Supply Chain Dynamics, Sustainable Suppy Chain
Abstract: In the food packaging industry, waste, resource utilisation, and transport are critical issues that significantly influence the food supply chain footprint. Several studies evaluated the Life Cycle Assessment (LCA) of food packaging systems by comparing alternative materials and sizes, such as plastic, cardboard, and wood. This study illustrates the results of a LCA conducted on a novel reusable plastic container (RPC) for fresh products travelling within a logistic network made of growers, retailers, package manufacturers, and RPC poolers. It is based on an Italian country-wide distribution system of fruit and vegetables. The novel contributions of this research deal with the utilisation of primary data and the development of a multi-scenario analysis considering different packaging solutions, production recipes, and energy mix at the production plant.

Keywords: Sustainable Manufacturing, Sustainable Suppy Chain
Abstract: The plastic litter issues make urgent the implementation of circular economy practices in the packaging sector. To this purpose, the present study environmentally analyses, through a Life Cycle Assessment, three plastic film alternatives used in Europe for pallets wrapping. Considering the impact categories of the Environmental Product Declaration (EPD) 2018 methodology, a traditional linear low-density polyethylene (LLDPE) film was compared not only to a thinner film with 60% post-consumer recycled LLDPE but also to a thinner high pre-stretch film. The ISO 14040 and 14044 as well as the Packaging Product Category Rule were followed in the study. Primary data of the films life cycle were collected thanks to the collaboration of a plastic film producer, while secondary data were modelled through the Ecoinvent database, available in the SimaPro software used in the study. The results highlight that the best option to reduce the environmental impact of these films is related to the reduction of plastic use thanks to lower thickness and higher stretching.

Keywords: Green Supply Chain, Sustainable Suppy Chain, Supply Chain Management (SCM)
Abstract: Nowadays organizations are under increasing pressure to adopt sustainable solutions for material handling and management due to growing environmental awareness. This drive has spurred advancements in innovative technologies, optimizing logistic processes to reduce energy usage and emissions, and enhancing overall supply chain sustainability. This approach extends to internal material handling with forklifts and the emerging use of drones for last-mile operations. Despite the importance of sustainable logistic practices, the literature lacks a comprehensive review of research on forklifts and drones in logistic activities. Through a Systematic Literature Network Analysis (SLNA), this paper addresses this gap by conducting a comparative analysis of two bibliometric reviews: one on forklifts' sustainable logistic applications and the other on drones'. This analysis aims to identify key contributors, countries, journals, and research themes in both areas. The SLNA results seek to highlight existing literature, productive countries, influential authors, and research themes. This paper provides theoretical contributions by identifying gaps in the current literature and highlighting key research themes related to sustainable logistic practices. Furthermore, it offers practical insights to industry stakeholders on integrating these practices using forklifts and drones, while also laying the groundwork for future research through a comparative analysis.

Keywords: Manufacturing System Engineering, Sustainable Manufacturing
Abstract: Plastic-based flexible films are widely used in many areas of modern industry, including the food industry. Depending on the application field, mono-material or multi-material films can be used, usually based on polyolefins such as PE and/or PP. Compared to mono-material films, multilayer films offer greater performance and functionality in terms of mechanical and barrier properties, thanks to the different types of polymers used. Considering the end-of-life of the two types of materials, the recyclability of mono-material film turns out to be much greater than that of a multilayer, since only one type of polymer can be handled in specific and specialized streams. The biggest issue with multilayer films is precisely end-of-life management, which currently appears to be mainly landfilling or waste-to-energy. In recent years, the use of increasingly sustainable packaging has become a prerequisite. The issue of recycling is of fundamental importance because of the implementation of a circular economy. This review aims to investigate recycling methodologies for both mono-material and multi-material films, from traditional ones, such as mechanical recycling, to more modern technologies not widely used in industry, such as delamination and chemical recycling.

Keywords: Product and Process Oriented Approaches, Human-Automation Integration, Enterprise System Engineering
Abstract: Digital transformations (DT) as part of currently implemented Industry 4.0 projects represent a key role in the competitiveness of manufacturing companies. However, the boundary conditions of a large company must be distinguished from those of a small and medium sized enterprise (SME). This means that an SME has a smaller specialist staff, tighter budgets and less market power available to implement DT projects internally and considering the prevailing conditions. This paper presents a methodology for handling a digitalization project in an SME. Due to its generality, this can be adapted to further use cases. The methodology was developed together with ATA Anlagentechnik (ATA), an SME in special purpose machinery sector. Focused on the internal process of technical sales, the method was used to optimize order data management. This was done through analysis and initial implementations in the areas of object recognition and the creation of efficient user interfaces in order to subject sales sub-processes such as the collection of customer input data to the creation of offers to a digital transformation. Once put into operation and supplemented by further internal or external processes, ATA moves towards complete digital data continuity, which represents an uninterrupted and consistent chain of business processes using digital data and tools. The paper outlines future steps to further refine what has been achieved using the presented DT method.

Keywords: Optimization and Control, Inventory Control and Dynamic Pricing, Supply Chain Management (SCM)
Abstract: With the development of electronic commerce and information technologies, many enterprises employ advance selling through online channels. This paper considers a farmer who grows and sells agri-products directly to customers via an online channel. We investigate the optimal selling strategies to maximize his profit. To this end, we first model the dynamic quality of the Agri-products during the entire life cycle. Three periods are taken into consideration: the growing process with increasing product quality, the non-deteriorating process with the constant and maximum product quality, and the deterioration process with decreasing product quality. Customers decide to purchase based on the quality and price of the product. In accordance with the quality dynamics, the optimal joint decision of selling price and time for three periods are investigated: advance selling, regular selling, and clearance selling. We provide managerial insights on whether or not to employ advance or clearance selling, and how to adjust the price and time. The value of different selling strategies is further presented via a numerical analysis.

Keywords: Inventory Control and Dynamic Pricing, Demand Forecasting, Supply Chain Management (SCM)
Abstract: This study examined the effects of hyperinflation on inventory decisions using a real-world dataset obtained from a leading retail company in Sri Lanka during the hyperinflation period of 2022 and developed a Machine Learning model to forecast optimal order quantities with the intention of lowering inventory holding costs. Six distinct ML techniques were selected to identify the best ML model. Root Mean Squared Error and R Squared values were used to rigorously evaluate the performance of the six ML techniques. The results suggest Random Forest as the most appropriate ML model to forecast optimal order quantities during a high inflation situation.

Keywords: Condition-Based Maintenance, Predictive Maintenance, Engineering Applications of Artificial Intelligence
Abstract: The automatic generation of maintenance orders facilitates the timely detection and root cause analysis of deviations or failures in the assembly process. The aim of this project is to use supervised learning models to detect deviations in the lead times of a fully automated robotic assembly process. The model identifies failures, categorises their causes and feeds the information back to the Enterprise Resource Planning (ERP) system. The data collected comes from a development and test assembly station with an industrial robot. The data set from the assembly station was extended using agent-based simulation to train the four models: Support Vector Machine, K-Nearest Neighbour, Naive Bayes and Decision Tree. The SVM model proved to be the most suitable model for automatic failure detection with an accuracy of 99.51 %. The model was integrated into the assembly station and an algorithm was developed to automatically generate maintenance messages to transmit the failure code to the ERP system.

Keywords: Robust Scheduling, Inventory Control and Dynamic Pricing, Uncertainty Modelling
Abstract: Lead time is a rather uncertain factor in manufacturing and inventory systems. In fact, it is always difficult to know the full information of stochastic lead time distribution. So a problem arises how a decision maker decides its ordering policy including advance ordering time, based on historically limited information. In this paper, this problem is solved under a robust-optimization framework, and the minimax regret criterion is adopted. We first derive the decision maker's robust ordering policies only with the range or the mean of stochastic lead time distribution respectively. The corresponding maximum regret, in other words, the value of full information is then derived. It is shown that when the decision maker uses the range to make decisions, this robust ordering policy exactly minimizes the realized costs if the average of the lower and higher limits of the range equals the realized lead time. When the decision maker uses the means to make decisions, the uncertainty of lead time distribution would lead the decision maker to place the order less in advance. Besides, this uncertainty would cause significant additional costs, especially when the mean is large.

Keywords: Manufacturing System Engineering, Robotic Systems, Decision Support System
Abstract: This paper delves into Virtual Commissioning's (VC) crucial role in accelerating aircraft manufacturing amid shorter product life cycles, employing the Software Platform Embedded Systems (SPES) methodology. Developing an adaptable VC architecture tailored for this industry, the study reveals SPES's efficacy but highlights areas for improvement. Despite its strengths, limitations in decision support and clarity have been identified, recognizing the dynamic nature of industrial scenarios. Offering a valuable blueprint, the presented systematic development process aids researchers and practitioners in the evolving domain of VC and automated production systems. The developed architecture demonstrates the incorporation of complex industrial requirements. However, as the requirements are generic, such as scalability, the architecture is also applicable to other application scenarios.

Keywords: Reconfigurable Manufacturing Systems (RMS), Flexible and Reconfigurable Manufacturing Systems
Abstract: While Reconfigurable Manufacturing Systems (RMS) were adopted to bolster resilience and customer need changes, notable obstacles endure, such as insufficient integration into operations and inadequate backing for distributed control. The Horizon Europe MODAPTO project aims to revolutionize manufacturing by proposing adaptable production systems enhanced with distributed intelligence through interoperable Digital Twins (DTs). By seamlessly integrating DTs based on industrial standards, MODAPTO promotes modularity, scalability, and sustainability. Addressing challenges such as incomplete integration and a lack of distributed control support, MODAPTO envisions a holistic approach to reconfigurability, considering economic, energy, environmental, and societal aspects. By leveraging cutting-edge technologies, MODAPTO seeks to offer a comprehensive framework to enhance adaptability, resilience, and sustainability in RMS.

Keywords: Flexible Manufacturing Systems (FMS), Mathematical Approaches for Scheduling
Abstract: This paper addresses the integrated flexible job shop and operators scheduling problem, introducing shift-based constraints on operators. We investigate how the advanced modeling and solution techniques, specifically, Mixed-Integer Programming (MIP) and Constraint Programming (CP) perform on this intricate scheduling problem. We test the effectiveness of both the MIP and CP models on an illustrative example as well as on a set of larger instances and draw conclusions, which indicate the need to integrate these two models into some approximation scheme to tackle large-scale instances for this complex problem.

Keywords: Supply Chain Management (SCM), Decision Support System
Abstract: The advancements and accessibility of AI technologies offer a lot of opportunities for supply chain management (SCM) research to address current challenges, such as environmental, societal and geopolitical needs. While the prominence of e.g., reinforcement learning has been applied for quantitative optimization techniques, the opportunities for qualitative data and approaches have not yet received a lot of emphasis. This new situation (with LLMs) enables joint research approaches to build on capabilities from AI technology together with supply chain management research. To ensure effective, scalable, and trustworthy AI systems in SCM a structured approach is imperative. In this paper, starting from current supply chain AI research, a methodological framework is proposed to build on both AI and SCM capabilities to support qualitative research along the SCM research cycle.

Keywords: Ergonomic Aspects of Industrial Technologies, Real-time Evaluation of Fabrication, Manufacturing System Engineering
Abstract: The central role that the human operator assumed in industrial design and development processes, driven by Industry 5.0 principles, has paved the way for the creation of new human-centric system architectures. This paper discusses the practical implications regarding the deployment of a Human Digital Twin-based system in the manufacturing environment. It provides a proof-of-concept of the architecture for the implementation of the twinning process, starting from the investigation of the most suitable hardware selection based on the desired outcomes to build up the human monitoring phase. We analyze three management decision-making levels to determine the scalability of the proposed architecture for strategical, technical, and operational managerial strategies. This research aims to propose some technological selection criteria, based on the main characteristics of the available technological acquisition devices, to determine the most suitable sensors for the creation of the physical twinning monitoring process.

Keywords: Human-Automation Integration, Process Planning/Equipment Selection, Co-operative control / manufacturing
Abstract: With the rise of Industry 5.0 and a shift to human-centric manufacturing, companies must successfully include their frontline workers when introducing assistance systems. While many frameworks address technical aspects or strategic management considerations of technology introductions, human- and organisational factors have been considered less. We contribute to the literature by comparing perspectives from workers and management on the introduction of two assistance systems. We uncover multiple findings, e.g. that a technology champion concept should be institutionalised or that the communication of the price point of technological systems can lead to intimidated employees and potentially slower technology adoption.

Keywords: Human-Automation Integration, Sustainable Suppy Chain, Robotic Systems
Abstract: This paper presents a systematic literature review exploring the impact of human–robot (HR) systems on older workers in manufacturing and logistics industries, drawing insights into how these systems can be optimally designed to address human factors in four key areas—perceptual, physical, cognitive, and psychosocial—aligning with the goals of Industry 5.0. By analyzing 19 articles selected from the Scopus and Web of Science databases, we identified both the benefits and challenges that HR systems pose to the aging workforce. We conclude that to achieve Industry 5.0 goals, HR systems must be designed to balance automation with human skills, promoting adaptability and inclusivity tailored to the diverse responses of older workers.

Keywords: Reconfigurable Manufacturing Systems (RMS), Mathematical Approaches for Scheduling, Dynamic Systems
Abstract: The adaptability of Reconfigurable Manufacturing Systems (RMSs) to ever-changing product demand and their responsiveness to unpredictable events has made them a popular solution for modern manufacturing in today's competitive industries. The proper scheduling of RMSs is important in ensuring optimal resource utilization, achieving production objectives, and responding to dynamic changes in a real-time. This study aims to tackle job scheduling and workforce planning in RMSs by considering dynamic events, such as the risk of machine unavailability, and human factors in the manufacturing process, to minimize the makespan and delay-related cost. At first, a comprehensive framework for dynamic optimization in an RMS environment has been developed. A multi-level bi-objective mathematical modeling presented to address job scheduling and workforce planning proposed as a MILP model. Finally, numerical studies evaluated two scenarios: risk and human factors addressed or not, for small to medium-sized cases. The results demonstrate a significant gap between these scenarios.

Keywords: Sustainable Manufacturing, Ergonomic Aspects of Industrial Technologies, Line Design and Balancing
Abstract: One of the United Nations’ Sustainable Development Goals is focused on decent work and growth which aims to reduce and, finally, remove all barriers for people with a form of diversity like disability. In such a context, manufacturing and production systems should be adapted by adopting specific equipment to help workers with disability while executing jobs according to the type of disability they report. Jobs must be properly planned since disabled workers have physical or cognitive disabilities and specific rights to work. Further, aiming to guarantee a real inclusion of workers with disability production systems should be designed to include these workers in the same working environment as workers without disability. This paper focuses on assembly systems, and it aims to investigate how different designs could impact both the productivity and inclusion of disabled workers. Then, due to the higher variety of products belonging to the same family mixed model assembly systems are considered. For each assembly system design, the daily productivity is calculated by using a simulation approach. Finally, according to the obtained results we provide some considerations about the convenience of adopting parallel flows to guarantee higher inclusion without affecting too much the productivity of the assembly system.

Keywords: Supply Chain Management (SCM), Decision Support System, Sustainable Suppy Chain
Abstract: Short Food Supply Chains have logistics costs almost four times higher than the average of the agri-food sector [8]. And yet, the environmental impact of small-size farms involved in such initiatives is believed to be much better, with less input and less soil depletion. With the agricultural sector responsible for 26% of CO2 emissions, economically viable and environmentally friendly initiatives in this sector can have huge impacts in terms of climate change mitigation. This paper proposes a simulation-based approach, applied on an existing short food supply chain. The performance of the system is measured and analyzed in three dimensions: economic, environmental, and social, with qualitative discussions on their correlations. The framework of indicators proposed is based on a literature review of usual KPIs for Food Supply Chains. We adapted the set of indicators in order to evaluate the performance of the actual organization, and simulate the impact of three optimization possibilities, chosen based on local opportunities. Our results enable us to facilitate decision-making for the organization, by visualizing the impact of each strategic decision on sustainability. The outcomes of this research provide input for further studies investigating the logistical organization in SFSCs, helping these initiatives become a viable and sustainable alternative to conventional food systems. For this paper, we limited the study to past available data regarding demand and supply, and we also limited the scope of the supply chain to the producers, customers, and association.

Keywords: Manufacturing System Engineering, Sustainable Manufacturing, Enterprise System Engineering
Abstract: This study deals with both resilience of production systems in the Industry 4.0 context and the urgent need to take sustainability into account in their management. The “transformative resilience” concept is discussed, leading to go beyond the classical view of resilience as a recovery approach after disruption. After a reminder of the need to sustainable production systems, the major principles of classical resilience are highlighted. Then, issues and features of the transformative resilience are presented, leading to emphasize the need to reconsider the “steady equilibrium” state in which production systems are and tend to come back to after disruption as well as to revisit the way industrial performance is currently evaluated and managed. Conclusion and remarks end this preliminary work that has to be continued into the development of an operational framework for integrating this resilience into the management of sustainable Industry 4.0 production systems.

Keywords: Closed-loop Supply Chains, New Product Development, Supply Chain Management (SCM)
Abstract: This research aims to highlight the opportunities for integrating product development (PD) processes and supply chain (SC) design decisions in the transition towards a circular economy (CE). For this purpose, the pivotal role of implementing the R-imperatives (recovering, recycling, and remanufacturing to reducing, rethinking, and refusing) in developing Fisher's model as a managerial tool for designing the SC for a specific product in a CE context, is investigated. This model has been designed using evidence from the literature, industrial workshops, and site visits conducted within Norwegian industries. According to the outcomes, there are three distinct scenarios for implementing R-imperatives in PD and SC design, each scenario highlights the evolving collaboration between PD and SC managers. The proposed model provides a tool allowing SC managers to start the transition towards CE by developing effective CE strategic plans that align with Fisher’s model, thus designing a more optimal SC for their products.

Keywords: Supply Chain Coordination, Supply Chain Management (SCM), Modelling for Cooperative Transportation Systems
Abstract: This paper investigates the collaborative logistics operations of small and medium-sized enterprises situated in rural areas, with a focus on leveraging Industry 4.0 technologies. Recognizing the crucial role of SMEs in rural development, the study explores collaborations with customers, government, and other businesses. The proposed framework integrates two phases: identifying collaborators and logistics operations and addressing challenges through Industry 4.0 technologies. Findings reveal opportunities for SMEs in reverse logistics, knowledge collaboration, transportation, warehousing, and resource planning. Challenges are addressed with Industry 4.0 solutions, outlining a meaningful approach to rural logistics collaboration. The paper concludes with managerial principles for successful implementation in the specific context of rural areas in Norway.

Keywords: Closed-loop Supply Chains, Intelligent Products and Lifecycle Management, Business Process Modeling
Abstract: Digital Twins are key elements to develop the complex digital systems required for an effective Circular Economy transition. This study explores the intersection of Digital Twins and Business Models within Industry 4.0 and offers a comprehensive theoretical background of the advancements in Digital Twins and their potentials to innovate Business Models. Through a literature review, we identify and discuss eleven Digital Twin-enabled Business Models. The study suggests the necessity of further research efforts to validate theoretical perspectives and identifies future research venues, such as the exploration of Digital Twin-enabled Circular Business Models.

Keywords: Uncertainty Modelling, Sustainable Suppy Chain, Optimization and Control
Abstract: As increase of delivery items, the overtime of truck drivers becomes an important issue, and so the effective operation of trucks is necessary. In this research, the effect of the discount fee on leveling the amount of necessary is discussed. By introducing the discount fee, some of demand corresponds to the late delivery, and leveling loads of trucks is realized. The mathematical model is formulated as a Markov reward model. In numerical examples, the optimal discount is derived and the effectiveness of introducing discount is discussed. Sensitivity analysis, on the number of normal trucks, the capacity for each truck and the demand function for the discount, is also developed.

Keywords: Supply Chain Management (SCM), Supervisory Control Theory, Optimization and Control
Abstract: Defining an efficient inventory management of Perishable Supply Chains (PSCs) becomes a very difficult problem when the "a priori" information on the perishability rate is affected by a large degree of uncertainty. In this paper we face this problem using a multi-model based supervisor and a reconfigurable min-max Robust Model Predictive Control (RMPC). The twofold task of the supervisor is: 1) identify the most likely uncertainty scenario, 2) drive towards it the current min-max MPC law.

Keywords: Inventory Control and Dynamic Pricing, Dynamic Programming, Heuristic and Metaheuristics
Abstract: We consider a joint discount and replenishment problem in a discrete periodic review fashion for the sale of a perishable product, characterized by limited deterministic shelf life, replenishment lead times, and stochastic demand. Customers decide what to buy according to a linear discrete choice model, balancing price and perceived quality, uniquely determined by the residual shelf life. The decisions we consider are: How many new items to order, the age of the items to be discounted, and how much discount to offer. In this context, we compare a set of policies mixing the constant order policy and the base stock one with some easy discounting policies, optimizing their parameters using a simulation-based optimization framework. To evaluate their performance in terms of revenue and quantity of scraped items, we consider four realistic instances for a grocery retailer characterized by products of different shelf life and variance of demand. Experiments show that best results are achieved by a base stock policy that discounts products of different ages based on a threshold: If the quantity of the inventory of a given age is greater than a threshold it applies a discount, otherwise no discount is proposed. In the presented configurations, this policy increases the average reward compared to policies that do not discount.

Keywords: Inventory Control and Dynamic Pricing, E-solutions in Operations Management
Abstract: There are many industrial applications where attempts are made to sell ancillary products or services with a primary item. For instance, a warranty is often offered as an ancillary product to those who buy the television sets (the primary item). This has become even more important in the airline industry where ancillary products such as baggage fees have become crucial to the financial health of the industry. Rapid depletion of available seat inventory, akin to perishable assets, necessitates careful consideration of pricing strategies to maximize revenue. It can make a big difference to revenue if certain fare classes are likely to sell out quickly. For instance, if most of the fares without seat assignment sell out earlier than those with seat assignment, the airline could forego a lot of the revenue from the ancillary item. We formulate the problem allowing for time dependence. In this paper, the question of what price to charge for the primary and ancillary products will be investigated. Keywords: Pricing of deteriorating inventories, perishable inventory, revenue management

Keywords: Supply Chain Management (SCM), Inventory Control and Dynamic Pricing, Supply Chain Coordination
Abstract: For businesses specializing in ameliorating goods, such as seasoned cheese, traditional financing models pose unique challenges. Inventory financing relies heavily on past performance, often overlooking the inherent value increase associated with proper aging. This can lead to limited access to capital, hindering growth and operational stability. Warehouse financing emerges as a specialized solution specifically designed for businesses with maturing inventory. Lenders recognize the future value potential of these goods, offering secured loans based on anticipated market appreciation. This approach unlocks immediate cash flow, empowering businesses to cover operational costs, invest in expansion, or manage cash flow fluctuations. This study develops and discusses inventory problems for the specific class of "ameliorating" products, integrated with the warehouse financing technique, to combine the two topics and highlight their main features but above all their scientific and practical importance. The models proposed are focused on a decentralized scenario (single actor perspective) and a centralized scenario (supply chain perspective) to compare the optimal solution in terms of the aging period while maximising the annual profit. Furthermore, from the supply chain perspective, a multi-supplier single-manufacturer supply chain is proposed with a deteriorating raw material (i.e., fresh milk). While cheese is a prime example, warehouse financing can benefit a diverse range of businesses dealing with ameliorating goods (such as wines, coffee, and aged spirits).

Keywords: Reconfigurable Manufacturing Systems (RMS), Process Planning/Equipment Selection, Mathematical Approaches for Scheduling
Abstract: The integration of process planning and scheduling(IPPS) is a crucial manufacturing strategy to optimize manufacturing resources utilization, which combines process planning and scheduling as an integrated function instead of two separated functions performing sequentially. Reconfigurable manufacturing system(RMS) is one of the latest manufacturing paradigms to meet unstable periodic market changes and environmental challenges. However, IPPS was seldom studied in context of RMS before. This paper proposed a multi objectives mixed integer linear programming model to solve IPPS problem in context of RMS, to optimize the makespan and total energy consumption in manufacturing. To be more realistic, the model considers transportation and reconfiguration factors, simultaneously meeting process planning flexibility.

Keywords: Real-time Artificial Intelligence, Self-learning Models, Engineering Applications of Artificial Intelligence
Abstract: With the increasing adoption of machine learning systems, concerns around bias and privacy have gained significant research interest. This work investigates the intersection of algorithmic fairness and differential privacy by evaluating differentially private fair representations. The LAFTR framework aims to learn fair data representations while maintaining utility. Differential privacy is injected into model training using DP-SGD to provide formal privacy guarantees. Experiments are conducted on the Adult, German Credit, and CelebA datasets, with gender and age as sensitive attributes. The models are evaluated across various configurations, including the privacy budget epsilon, adversary strength, and dataset characteristics. Results demonstrate that with proper tuning, differentially private models can achieve fair representations comparable or better than non-private models. However, introducing privacy reduces stability during training. Overall, the analysis provides insights into the tradeoffs between accuracy, fairness and privacy for different model configurations across datasets. The results establish a benchmark for further research into differentially private and fair machine learning models, advancing the understanding of training under an adversary.

Keywords: Decision Support System, Real-time Control, Signal Processing
Abstract: The paper presents an approach for the integrated use of expert systems and dynamic models in an intellectualized operator information support system (IOISS) for power units (PU) of nuclear power plants (NPP). The main functions of IOISS for operators of NPP PUs are identified, and the full-scale implementation of these functions based on predictive dynamic modeling of PU parameters and the use of expert systems is shown. The structure of the expert system, which is part of the IOISS operators, is considered. This approach to the IOISS of operators is used in developing upper level systems for instrumentation and control (I&C) systems for NPPs.

Keywords: Formal Specification and Verification, Manufacturing System Engineering
Abstract: Requirements engineering (RE) is essential for system design because incorrect or incomplete RE can lead to misunderstandings, gaps, and mistakes that can negatively affect projects. Higher-quality requirements can reduce errors. However, verification and validation (V&V) of requirements qualities is challenging. Formal modeling enables automation for early V&V of large requirements sets. The use of patterns improves requirements quality, but gaps still exist in formal modeling and V&V methodologies. This paper presents a system requirements conceptual model with requirements writing patterns that enable the systems requirements modeling and V&V, along with a comparative analysis with existing concepts in the literature.

Keywords: Human Resource Allocation, Reconfigurable Manufacturing Systems (RMS), New Product Development
Abstract: Research studies have rarely taken into consideration the challenge of resource selection and technician scheduling problems in the context of product evolution. Indeed, incorporating new product variants into the line’s future production generation has a significant impact on resource selection and technician scheduling. Additional resources may be necessary to produce new products, and each of these resources demands specialized skill for operation. Hence, it may be necessary to provide training for the technicians to enable them to effectively work with the new resources. This study aims to plan resource selection and technician scheduling for a line which produces a given product family with a set of product variants, and a new product variant will be added after a certain period of time, named production generation. At each generation, the production family can be changed by adding or removing a product variant. We develope a Mixed-Integer Linear Programming (MILP ) that minimizes the total resource selection and technician training costs for the worst-case scenario. The MILP is validated through solving a simple example and the results are given.

Keywords: Supply Chain Management (SCM), Integer Linear Programming, Sustainable Suppy Chain
Abstract: The Energy-Optimized Truck Door Assignment Problem (TDA) addresses the optimization of truck assignments to doors in cross-docking facilities with the aim of minimizing energy consumption and operational costs. In this paper, we propose a scalable optimization model for the TDA and evaluate its capacity to handle varying numbers of trucks and doors. The model integrates mixed-integer linear programming (MILP) techniques to formulate the assignment problem, considering constraints such as truck arrival times, door capacities, and energy-efficient routing strategies. Through a comprehensive analysis, we assess the scalability and effectiveness of the TDA model in optimizing truck door assignments and promoting energy-efficient logistics operations. Results indicate promising performance and highlight opportunities for further research in sustainable logistics management.

Keywords: Sustainable Suppy Chain, Information Sharing, Multicriteria Decision Making
Abstract: The growing demand for electric vehicles necessitates an efficient and sustainable life-cycle management of lithium-ion batteries. This work examines existent literature on digital battery passports, crucial for high-quality data for decision-making purposes, and distributed ledger technologies as transparent and efficient enablers. An hybrid BWM-TOPSIS approach is employed to rank various platforms for digital passport implementation in an automotive company. The analysis identifies Hedera as the most suitable ledger, followed by IOTA and EOS. Future research directions include empirical validation of the findings and exploring collaborative decision-making models to enhance the robustness of the selection process.

Keywords: Data Models, Intelligent Predictive Control, Risk Management
Abstract: System obsolescence represents a major challenge for industrial companies. To tackle this problem, two main strategies are used: reactive management, which seeks solutions when obsolescence occurs, and proactive management, which anticipates and mitigates obsolescence risks to optimize time and cost management. Effective risk assessment as part of proactive obsolescence management requires high-quality data, in-depth industry expertise and robust risk assessment methodologies. However, these requirements are not always met. The aim of this paper is to identify, through a literature review, the challenges involved in assessing and predicting obsolescence risk. We then propose a case study for predicting the obsolescence date of FPGAs, as an example of active electronic components. Through this choice, we will show the different prediction steps and the difficulties encountered in order to learn from them.

Keywords: Decision Support System, Multicriteria Decision Making, Manufacturing System Engineering
Abstract: Obsolescence is a major challenge for industries faced with evolving technologies. To remedy this problem, whose costs can reach significant levels, mitigation solutions exist and are often chosen with a view to financial optimization. This article presents an analysis of the various obsolescence management criteria, in addition to cost. The working team distinguishes and details mainly the obsolescence context-related criteria, which, to the best of our knowledge, have never been addressed in the literature. Industrial and academic experts collaborated to define the preferences between the criteria and establish a weighting, emphasizing the importance of the obsolescence context.

Keywords: Sustainable Suppy Chain, Green Supply Chain
Abstract: The current work presents a deterministic bi-objective sustainable crowdshipping problem optimizing both the cost and CO2 emissions, referred to as the Bi-Objective Vehicle Routing Problem with Multi types of Occasional Drivers and Time Windows (BO-VRPMODTW). The goals of this work are to verify the trade-off between transportation cost and environmental emissions generation, considering different types of ODs, and to verify the impact in different location spreading for customers. Preliminary experimental results show the gain both in terms of cost and CO2 emissions of having ODs of different types. The use of ODs in the non-dominated solutions is also analyzed.

Keywords: Predictive Maintenance, Genetic Algorithms
Abstract: In this paper, we present a grouping maintenance approach that allows efficiently considering economic dependence between components and also the impact of dynamic context such as changes in the degradation process, limited number of maintenance teams, occurrence of maintenance opportunities, etc. A real industrial case study is used to investigate the applicability and the robustness of the dynamic grouping maintenance approach in the maintenance optimization framework.

Keywords: Manufacturing System Engineering, Ontology for Enterprise Interoperability
Abstract: The traditional sequential aircraft design process is reaching its limits in an increasingly complex environment. Therefore, it has become an industrial necessity to implement radical change in working methods. This paper focuses on the conceptual design phase, where concurrent engineering offers the greatest potential to improve the costs, quality, and development time of a new aircraft. It pays particular attention to the knowledge management and modeling activities used to provide global perspectives that are inherently lacking in early phases. A digital thread framework is presented linking model-based systems engineering artifacts from product and manufacturing systems, coupled with knowledge capture methods. This framework aims to enable early trade-off analysis to support concurrent engineering during the conceptual design phase.

Keywords: Integer Linear Programming, Mathematical Approaches for Scheduling, Optimization and Control
Abstract: Rolling stock maintenance is a key issue for the railway operations. Planning cyclical maintenance activities is a fundamental aspect of maintenance management, requiring a variety of operational choices. We present a novel flow formulation approach to schedule rolling stock maintenance activities optimising both maintenance costs and train availability. The schedule takes into account cyclical maintenance activities and the decoupling between elapsed and operational time. Limited resource and depot capacity are taken into account together with operational constraints to reflect the real operations of the railway system. The proposed formulation is based on an integer linear programming (ILP) approach, which is solved using a commercial solver on randomly generated realistic instances.

Keywords: Engineering Applications of Artificial Intelligence, Intelligent Manufacturing Systems, Real-time Artificial Intelligence
Abstract: This paper explores the generation and use of synthetic data in the development of AI-based vision control systems within the automotive industry. Addressing the challenges of collecting diverse and high-quality datasets, we demonstrate the effectiveness of synthetic data in training deep learning models for defect detection and quality inspection. By leveraging controlled randomizations during image dataset generation, we mitigate the domain gap between synthetic and real-world data, enhancing model performance and generalization. Our approach, validated across multiple industrial applications, significantly accelerates the development cycle and improves the accuracy of vision-based inspection systems. This study highlights the potential of synthetic data to overcome data scarcity and improve the efficiency and effectiveness of AI-driven quality control in automotive manufacturing.

Keywords: Engineering Applications of Artificial Intelligence, Intelligent Manufacturing Systems, Human-Automation Integration
Abstract: The progress in efficient human-robot partnerships has influenced all facets of contemporary industry. A considerable proportion of manufacturing procedures, namely the final assembly and trimming processes, continue to be semi-automated since human agents possess superior skills in performing these tasks. Manufacturers have been progressively integrating augmented reality (AR) into their operations to equip, instruct, direct, and enhance the abilities of their less-experienced staff. As the proficiency of augmented human agents increases, their contact with the robot agents likewise undergoes dynamic modifications. This paper introduces a human-robot collaborative reinforcement learning model (HR-CRL) that enhances the decision-making power of robotic agents. The method considers the changing observable information from the production system environment and the input from human operators. The HR-CRL model is assessed across many scenarios of semi-automated manufacturing activities, which replicate fluctuations in augmented human performance and the environment. The evaluation showcases how robot agents adapt their behavior in response to observable data, enhancing the efficiency of the human-robot collaboration.

Keywords: Sustainable Manufacturing, Sustainable Suppy Chain, Integration of Knowledge/Competence in Enterprise Modelling Framework
Abstract: The European Green Deal and the European Innovation Council (EIC) aim to drive sustainability in manufacturing. Industry 5.0 emphasises the integration of automation with green skills, highlighting their importance for sustainability. However, which manufacturing areas need green skills and which green skills are crucial in manufacturing remains unclear. This paper aims to fill these gaps, by performing a literature review and an analysis of the European Skills, Competences, Qualifications, and Occupations (ESCO) database. Results identify five critical areas: Product-Process Design, Big Data Analytics and Artificial Intelligence, Supply Chain Management, Circular Economy, and Energy Management. These areas necessitate skills such as environmental impact assessment, waste management, data analysis, eco-design, and knowledge of renewable energy technologies. ESCO analysis indicates that 46.28% of 3008 occupations involve green skills, which are crucial for sustainable development. Targeted training programs are needed based on identified areas and skills. Interconnections among clusters and the importance of data analysis are highlighted. Additionally, in the near future manufacturing companies should attempt to develop broad, transversal skills integrated with digitalisation to meet an increasing demand for sustainability. Overall, the study provides insights into green skills development and their critical role in shaping the future of sustainable manufacturing.

Keywords: Cognitive Aspects of Automation, Ergonomic Aspects of Industrial Technologies, Human-Automation Integration
Abstract: Industry 5.0, built on the foundation of Industry 4.0, aims to integrate human capabilities and principles for a sustainable and human-centric industrial paradigm. This article proposes a systematic literature review to explore the empirical effects of Industry 4.0/5.0 (I4.0/5.0) technologies on human factors, emphasizing the often overlooked physical, psychological, and cognitive dimensions. The methodology, adhering to PRISMA guidelines, involved a Scopus search spanning from 2005-2023, ultimately resulting in a selection of 15 articles. The preliminary results depict the studied I4.0/5.0 technologies and their impact on workers’ physical, psychological, and cognitive aspects. Some (I4.0/5.0) technologies received significant attention (such as human-robot communication and human-robot interaction), while others remain understudied. The limited number of papers makes it difficult to compare and generalize the empirical results reported. In this regard, we suggest avenues for refining this systematic literature review in future research endeavors.

Keywords: Cognitive Aspects of Automation, Human-Automation Integration, Human Resource Allocation
Abstract: This paper analyses the past and anticipated developments in collaborative smart production work environment and points at the required skills to best utilize and flourish in this newly formed work environment. The paper identifies the new work requirements using the job type and its related required technologies, and maps the work requirements to the set of skills that may fulfill these requirements. An important notion in this paper is that a shopfloor usually involves several different work environments, each with its unique set of work requirements and associated skills. Thus, tailoring a subset of skills to these set of requirements is the suggested strategy. We use a small example of assembly shopfloor for illustrating the proposed approach. Finally, we propose future research related to this study.

Keywords: Ergonomic Aspects of Industrial Technologies, Cognitive Aspects of Automation, Human-Automation Integration
Abstract: Organizations as well as their employees are facing high stakes challenges in ongoing digital transformational processes. Both parties can benefit if human factors are addressed, and an innovation mindset is developed towards a continuous, agile approach towards emerging technologies, thus improving corporate culture, increasing technology acceptance and probability of persistent economic success. This paper presents the preliminary results of a case study of a global leader in mechanical engineering manufacturing employing a coaching approach during their strategic transformation. We highlight how development of individuals and teams through coaching can increase solidarity and positively affect corporate culture during transformational change, resulting in positive economic outcomes. Based on the results of the case study, we propose managerial implications and deduct ideas for future research on successful employee development during change processes to avoid adverse reactions in workforces.

Keywords: Human-Automation Integration
Abstract: Manufacturing workers' fatigue is an acknowledged concern with implications for well-being, health, safety, and operational performance. Past studies have employed physiological measurements obtained from smartwatches and wearable devices, seeking to assess and classify the fatigue state of workers. However, the extent to which models developed based on data obtained from individual workers could apply to other workers remains unclear. This paper presents the results of an exploratory study in which data from different subjects are employed to develop a range of fatigue estimation and predictive machine learning models. A cross-subject study provides evidence of sufficiently accurate performance in several cases. Further insights arise from looking into cases of lower generalization and linking these to personal characteristics.

Keywords: Integration of Knowledge/Competence in Enterprise Modelling Framework, Information Sharing
Abstract: In the context of industry 4.0 and 5.0 where humans are placed at the heart of approaches, organizations such as manufacturing systems have to manage their digital assets in order to be more competitive. Data, information, experiences and knowledge about problems which can occur during processes have to be extracted, formalized and capitalized along with the solutions for a reusing purpose. The main objective of this article is to propose a method to transform implicit personal knowledge used by individuals during their projects into explicit personal knowledge (Personal Knowledge Graphs - PKG) using a Personal Knowledge Management (PKM) approach. Second, a generalization approach based on knowledge items co-occurrences is proposed in order to transform several Personal Knowledge Graphs into an Organizational Knowledge Graph (OKG) suitable for Organizational Knowledge Management (OKM).

Keywords: Embedded Devices
Abstract: In recent years, Extended Reality (XR) technology has emerged as a potent instrument, inducing transformative effects across various industries by providing immersive experiences and expanding accessibility for individuals encountering physical impediments. The merging of XR technology with hyper-realistic avatars and innovative interfaces exhibits significant potential in reshaping rehabilitation methodologies and elevating the quality of life for individuals afflicted with profound mobility and verbal communication diseases. Within this framework, the Social and hUman ceNtered XR (SUN) project is dedicated to investigating and advancing XR solutions that seamlessly integrate the physical and virtual words, with a specific emphasis on the human and social dimensions. The project endeavors to overcome challenges associated with hyper-realistic human avatar, constraints posed by end-user device resources, the incorporation of wearable haptic technologies to augment physical interactions and the exploration of gaze-based and gesture-based interaction modalities. This work will describe the architecture of the pilot 3 of the SUN project, XR for people with serious mobility and verbal communication diseases, focusing on the implementation of the XR Environment.

Keywords: Human-Automation Integration, Robotic Systems
Abstract: The metaverse concept has become much more relevant in recent years. The three pillars of industrial training, safety, and social interaction are the framework around which this article organises evaluates research into extended reality and metaverse technologies. It illustrates how diverse approaches may be used across various applications by highlighting its potential to enhance knowledge acquisition, work protection and well-being, and social engagement in industrial settings. To fully realise the potential of metaverse technologies, further study is necessary to address identified problems, including privacy concerns and the need for adapted policies to guarantee successful acceptance and implementation.

Keywords: In-process Manufacturing Monitoring, Human-Automation Integration
Abstract: With the adaptation of Industry 4.0 and emerging ideas of Industry 5.0, the manufacturing industry is shifting back towards human-centered production. In these scenarios, collaboration robots (Cobots) become an important part of this shift toward human-robot collaboration (HRC). However, with the introduction of robots meant to work in close proximity to and in collaboration with people, problems of safety and collaboration efficiency arise. On the other hand, there are developments in the field of Augmented Reality (AR), which allow overlaying digital information onto the physical environment and having people interact with it. This paper introduces an AR interface for HRC with cobots, which can tackle the issues of human safety and efficiency. The use of the interface is demonstrated on an HRC ski assembly use case. All the source code can be found on GitHub: https://github.com/ut-ims-robotics/rybalskii-incom-2024-replication-package

Keywords: New Product Development, Computational Science, Data Models
Abstract: Three dimensional digital avatars can be valuable assets in various industrial fields: from telepresence, entertainment, fashion, to wellness and medicine. Accurate geometric representations of a person’s body shape are crucial for extracting reliable anthropometric information applicable in contexts ranging from virtual try-ons to medical-grade applications, where precision can significantly impact outcomes. This paper presents two different automated procedures to perform anthropometrics measurements, both based on an non-rigid, human template mesh registration method that requires no human supervision, centered around a custom human body model. Both procedures were evaluated using datasets from six participants, comparing a fully automated registration with a manual registration performed by a trained operator. The results, analyzed and benchmarked against manual digital tape measurements, confirm that the accuracy of the automated methods aligns closely with ground truth, affirming the reliability of these measurements across diverse body shapes. This highlights the potential of fully automated methods in applications that require precise body measurements.

Keywords: Supply Chain Management (SCM), Inventory Control and Dynamic Pricing
Abstract: This study presents the results obtained from a survey intended to assess how consumer behavior towards perishable items is affected by discount policies and promotional strategies. To this aim, a survey was designed to investigate all the factors that, based on the findings of the scientific literature, can influence purchasing decisions. The survey was submitted to a sample of 496 consumers, randomly selected among the Italian population. From the results obtained, practical suggestions for the development of analytical and numerical models can be derived, thus allowing simulation and optimization of inventory management policies for perishable items, coupled with pricing policies aimed to minimize waste and maximise profit.

Keywords: Supply Chain Management (SCM), Inventory Control and Dynamic Pricing, Quantitative Modeling of Facility Design
Abstract: This study investigates hydrogen as a clean and versatile energy carrier. Doing so addresses the need for sustainable energy and bridges a research gap in the literature: the efficiency of centralized versus decentralized hub systems. The challenge of hydrogen supply chain configuration is explored, seeking to minimize its financial and ecological effects. The contribution of this work lies in a comparative analysis using a bi-objective optimization model underpinned by simulations for transportation and storage logistics. Results indicate that the centralized system had better outcomes for cost and emissions when compared to the decentralized system in the simulations conducted.

Keywords: Modelling Supply Chain Dynamics, Supply Chain Management (SCM), Process Planning/Equipment Selection
Abstract: This paper deals with integrated production-inventory-routing decisions in mining industries. It treats the impact of phosphate extraction routings adopted in open pit mines in providing stocks with diversified characteristics, to satisfy customer requirements at a minimum production cost. Those routings aim to control minerals' characteristics in terms of quality, volume, and delivery time. For planning production activities, there is a need to select the right extraction routings in mines, to enrich (or not) the raw material and then blend ingredients according to their availability and customer requirements. Less attention was given by literature to the impact of extraction routings in production and stock dynamics. The primary focus revolves around filling the gap and recognizing their pivotal impact in open pit mines. While washing and blending ingredients play crucial roles, they alone may not always suffice to meet customer requirements. This paper undertakes an exhaustive study and modeling, using the case study of a Moroccan company specialized in phosphate rock production and its derivatives, aiming to underscore the significance of extraction routings through tangible and real examples. The findings emphasize a crucial need for variating the extraction routings in providing stocks of raw materials with the optimal characteristics regarding customer requirements.

Keywords: Supply Chain Management (SCM), Optimization and Control, Integer Linear Programming
Abstract: This paper focuses on developing an investment strategy to enhance the resilience of healthcare systems. This problem, called p-median fortification problem, has been introduced by Starita and Paola Scaparra (2022). It aggregates two sub-problems which are to minimize the travel cost and to minimize the number of patients not treated by the system. The problem is formulated as a non-linear model to determine an effective distribution of protective resources among facilities in service or supply system to improve the system reliability. In this paper, we present a new linear approximation of the p-median fortification problem based on log-linearization techniques. The proposed approach is tested based on a benchmark inspired form the literature and the obtained results illustrate the effectiveness the new approximation approach.

Keywords: Manufacturing Resources and Processes, Process Planning/Equipment Selection, Discrete-event Simulation
Abstract: This study addresses the crucial role of Time Data Management (TDM) in switchgear and control cabinet manufacturing, sectors integral to industrial automation and characterized by custom-made or small-batch production. The research highlights the challenges in accurately forecasting production times in these domains, where products often have unique, non-repetitive characteristics. This study systematically categorizes production to ensure precise TD collection and analysis. A possible approach to logically divide control cabinet production into distinct manufacturing areas and workflow structures is presented, facilitating a multiple case study method and a comparative TD analysis across different companies. The contribution of this study is threefold: providing detailed workflow structures for an under-researched industry, offering empirical data for academic and practical assessment, and enabling efficient TDM in ETO industries, traditionally hampered by process measurement difficulties and reliance on expert time estimates. This research promises significant advancements in TDM, ensuring more accurate production planning, and cost calculation, and enhancing market competitiveness in switchgear and control cabinet manufacturing.

Keywords: Factory and Industrial Automation, Human-Automation Integration, Manufacturing System Engineering
Abstract: Improved functionality rather than cost has been the main driver for industrial digital systems developments over the last 30 years. Given the large number of outstanding challenges this has been a logical direction to take but has meant that many industrial companies - especially small ones - do not have sufficient budget to add digital solutions to their operations. The field of low-cost industrial digitalisation which has emerged over the last ten years in which cost is given a higher priority than functionality. This paper charts the evolution of the field and discusses the scope and applicability of such low-cost solutions and argues that a systematic approach to specification, design and deployment of these solutions can be effective in increasing the confidence of industrial organisations in applying them.

Keywords: Enterprise-wide Information System, Real-time Artificial Intelligence, Decision Support System
Abstract: The manufacturing sector is undergoing a substantial digital transformation. Following decades of developments in data capture technologies, computer science, and IT infrastructure, manufacturers collect and store more data than ever. However, collecting data is one challenge; another is using it. Many manufacturers are struggling to take advantage of the possibilities offered by advanced analytics. This paper introduces a new category of industrial IT system that solves this problem: the Manufacturing Analytics System (MAS).

Keywords: Sustainable Manufacturing
Abstract: Small and medium-sized enterprises (SMEs) are critical to both the global economy and the environment. However, these businesses often face obstacles when it comes to implementing sustainable practices. That's why SMES must manage their resources efficiently, assess their current situation, and identify opportunities for improvement. To support SMEs in this effort, a research paper has been developed to offer relevant information and guidance for policymakers and representatives on effective sustainability measures. This paper is based on a sustainability assessment of SMEs in various sectors and countries throughout Europe. It provides insight into SMEs' status quo, target scores, and sustainability gaps. Findings reveal significant efforts to implement measures in the economic dimension, while the management and ecological dimension shows the highest potential for improvement. These insights can aid industrial representatives and policymakers in developing targeted measures to establish sustainable measures within SMEs, emphasizing the importance of understanding their sustainable transition comprehensively.

Keywords: Sustainable Manufacturing, Performance Evaluation, Manufacturing System Engineering
Abstract: Climate change and industrialization have led to major environmental challenges. Manufacturing Small and Medium Enterprises (SMEs), particularly in Canada, play a crucial role in this context, contributing significantly to emissions and energy consumption. Industry 4.0 (I4.0), with its technological advancements, offers opportunities to improve the environmental impact (EI) of SMEs. However, the increase in automation and digitization raises questions about their true ecological impact. This paper highlights the existing gap in the literature between environmental indicators and digital transformation (DT), making it difficult for SMEs to measure and improve their ecological footprint. This research aims to bridge this gap by identifying and compiling relevant environmental indicators for SMEs undergoing digital transformation. The paper's findings categorize environmental indicators into three groups: resource usage, environmental repercussions, and durability. These indicators provide a framework for assessing the EI of SMEs in DT.

Keywords: Intelligent Manufacturing Systems, Sustainable Manufacturing
Abstract: This paper explores the landscape of Digital Twins (DTs) in smart and sustainable manufacturing through a systematic literature review. In particular, the paper aims to identify the widespread trends and intersections of Digital Twin (DT) applications, maturity levels, and sustainability dimensions, by means of a three-layer heat-map chart. Observations indicate a concentration of DT applications in the production side of manufacturing, with notable impacts on economic and environmental sustainability. However, major deficiencies in addressing social sustainability persist in overall applications. Further insights suggest that DT research requires ongoing advancements in the establishment of granular maturity levels, in response to the need for a clearer categorization and definition of DTs.

Keywords: Genetic Algorithms, Scheduling Heuristics, Robotic Systems
Abstract: The Flexible Job Shop Scheduling Problem (FJSSP) is a challenging issue for industries and manufacturers. However, the transportation tasks in this problem should not be underestimated, as their impact is substantial, and they are frequently overlooked in the literature. In this paper, the FJSSP with multi-AMR (Autonomous mobile robot) transportation constraints is addressed. We developed a genetic algorithm with dispatching rules to solve the Flexible Job Shop Scheduling Problem with Multi-AMR Transportation Constraints by minimizing the total completion time. Two dispatching rules are used to assign transportation robots. The first rule is random assignment and the second rule is AMR per Job assignment. Our encoding and decoding process utilizes a three-layer sequence (machine sequence, operation sequence, and AMR sequence). Finally, we conducted comprehensive experiments on different benchmarks to assess the performance of the proposed algorithm with a variable number of AMR to transport the operations. The experimental results indicate that the proposed algorithm effectively and efficiently solve the problem.

Keywords: Quality Predictive Monitoring, Optimization and Control, Intelligent Predictive Control
Abstract: Manufacturing processes are susceptible to quality defects, resulting in reduction of overall equipment effectiveness. Proactive and reactive methods such as process failure mode and effects analysis and root cause analysis exist to analyze and mitigate potential causes of failure modes. In this study, data from an assembly case is analyzed using supervised machine learning methods to investigate the challenges of purely data-driven failure mode detection. Assembly step execution times as indicators and end-of-the-line quality checklists as the failure modes have been used to gain insight into failure mode detection. Challenges for data-driven methods are discussed and possible future research directions are proposed.

Keywords: Machining and Assembly Systems, Line Design and Balancing, Sustainable Manufacturing
Abstract: In response to sustainability imperatives, manufacturers adapt production lines for enhanced energy efficiency. This study focuses on flexible transfer lines (FTL), which are renowned for flexibility and efficiency in mass-customized production. This study addresses the pivotal challenges of balancing and scheduling FTL, aiming to optimize both cycle time and total energy cost. A novel mixed-integer linear programming model and a multi-objective optimization approach utilizing the epsilon-constraint method are introduced for solving small to medium-sized problems. The findings advance sustainable practices by exploring the impact of varying energy modes on FTL sustainability, offering manufacturers insights into energy-efficient production strategies.

Keywords: Intelligent Manufacturing Systems, Sustainable Manufacturing, Real-time Artificial Intelligence
Abstract: This research explores the integration of digital twin technology with circular manufacturing principles using a behavioral modeling approach. We propose a digital twin framework to enhance sustainability and efficiency in manufacturing, focusing on extending product lifecycles and promoting recycling. Employing synthetic data, our findings demonstrate how digital twins can optimize resource use and support sustainable practices. The study emphasizes how digital twins can revolutionize environmental sustainability in manufacturing.

Keywords: Uncertainty Modelling, Intermodal Transport, Decision Support System
Abstract: Supply chain resilience analysis aims to identify the critical elements in the supply chain, measure its reliability, and analyze solutions for improving vulnerabilities. While extensive methods like stochastic approaches have been dominant, robust optimization—widely applied in robust planning under uncertainties without specific probability distributions—remains relatively underexplored for this research problem. This paper employs robust optimization with budget-of-uncertainty as a tool to analyze the resilience of multi-modal logistics service networks under time uncertainty. The interactive effects of three critical factors are examined: network size, disruption scale, disruption degree. The computational experiments offer valuable managerial insights for practitioners and researchers.

Keywords: Physical Internet, Sustainable Suppy Chain, Multi-Agent Systems and Heterarchical Topology for Manufacturing
Abstract: The efficient and sustainable movement of goods within the supply chain is a cornerstone of global trade, and the Physical Internet (PI) paradigm presents a transformative approach to achieving this goal. This paper examines the environmental impact of empty containers (EC) within a water-road hub in the context of PI. An agent-based simulation approach through negotiation in Netlogo is proposed and applied to the multi-modal hub "Halluin II" in the north France. Results indicate that PI-containers significantly lower CO2 emissions by 27%, with ANOVA testing confirming the statistical significance of these outcomes. Furthermore, the analysis of simulation results highlights a significant improvement in efficiency when using PI-containers. This implies that the PI model not only enhances environmental sustainability but also improves operational efficiency.

Keywords: Information Sharing, Modelling Supply Chain Dynamics, Optimization and Control
Abstract: This paper explores the impact of information-sensitive retailers on a manufacturer's decision to adopt blockchain technology in supply chain management. A mathematical model is developed, considering factors such as retailer information sensitivity, sensitivity costs, and the manufacturer's profit function. The model aims to optimize total profit by setting production quantity and price while accommodating both sharing and non-sharing retailers. Analysis of the manufacturer's optimal equilibrium strategy highlights the critical role of information-sensitive retailers in blockchain adoption decisions. Findings suggest that the proportion of such retailers should satisfy specific conditions for optimal outcomes, emphasizing the importance of maintaining a balance in information-sensitive suppliers for efficient information sharing and manufacturing costs. Additionally, the paper conducts a comprehensive sensitivity analysis, varying each parameter to assess its impact on the profit function. A numerical example illustrates the practical implications of the model. The insights derived from the sensitivity analysis provide valuable managerial strategies for enhancing supply chain efficiency and increasing manufacturer profitability.

Keywords: Inter-Organizational Workflow Management, Supply Chain Coordination, Holonic-based Manufacturing
Abstract: Small and Medium-sized Enterprises (SMEs) face steep challenges in fulfilling sustainability and resilience requirements when joining global supply chains. These challenges often relate to lack of capabilities, accreditations and/or capacity demanded by OEMs and Tier 1 players in the process of forming a supply chain composition. To address some of the challenges and lower entry barriers for SMEs, this paper formalises a Collaboration Design Method (CDM) that can assemble and coordinate supplier teams responding to a tender from a large buyer. CDM comprises five recursive design decisions: (1) Decomposing tendering goals; (2) Assigning sub-goals to suppliers in a team; (3) Operationalizing sub-goals through supplier processes within a team; (4) Decomposing processes to identify steps that can be outsourced; and (5) Defining coordination mechanisms. Feedback from industrial SMEs indicates that CDM provides a viable approach to tame supply chain formation complexity and ease barriers that prevent SMEs from collaborating in global supply chains.

Keywords: Physical Internet, Integer Linear Programming
Abstract: Cross-docking is a just-in-time logistics strategy that involves the seamless transfer of goods from receiving docks to shipping docks without the need for extensive storage. It optimizes the order picking process by efficiently moving freight with the same destination, regardless of their original suppliers. This paper delves into optimizing operations within a rail-road PI-hub cross-docking terminal, which includes an internal storage zone. The primary goal is to enhance the flexibility and sustainability of workflows within the cross-dock platform. Scheduling revolves around assigning PI-containers, unloaded from train wagons, to outbound trucks and their corresponding docks within a specified timeframe.The objective is to concurrently minimize storage costs, energy consumption costs of PI-conveyors during container movement, and the overall trucking expenses required to meet demand at various destinations. To address this, a Multi-Objective Mixed-Integer Programming model (MO-MIP) is formulated and solved using the CPLEX solver with Lexicographic Goal Programming. The model's efficacy is demonstrated through testing on five distinct instances, where the CPLEX solver successfully identifies optimal solutions within reasonable computation times.