Keywords: Decision-support for human operators, Facility planning and materials handling, Smart manufacturing systems
Abstract: Order picking, a key warehouse process, is known to be costly, monotonous, and tightly regulated by a central planning system. Recent studies have explored a way to mitigate the negative impacts of order picking; they experimentally investigated the feasibility and effects of a participatory order assignment (POA) system, which allows workers to choose their next order and break the monotony rather than being confronted with top-down systemic assignments. Thus far, studies have mainly looked at the effects on productivity and psychosocial and physical well-being shortly after such an autonomy-increasing system redesign, yet little is known about the longevity of these effects. The existing literature emphasises the need for evaluations that go beyond short-term findings to assess effects over a sustained period. The degree to which the observed effects persist over time significantly impacts potential adoption by practitioners. By conducting a unique field experiment in a real-world warehouse, this paper assesses the effects of a POA system over a period of 6 months and finds that the initially observed effects persist over time. We enhance the existing body of literature by providing evidence that POA systems are not only effective, but they also yield long-lasting effects.

Keywords: Operations Research, Optimization and Control, Supply Chain Management
Abstract: This study investigates the evolving concept of competitiveness in logistics, emphasizing the role of digitalization, technological innovations, and strategic resource management in enhancing operational efficiency and securing a competitive advantage. The research traces the historical development of logistics competitiveness, from early market competition theories to modern strategies incorporating digital technologies. It also explores the impact of digital investments on logistics operations, offering insights into integrating digital solutions to improve operational efficiency. A two-level methodology is applied to analyze Information and Communication Technology (ICT) applications in Lithuanian third-party logistics (3PL) companies, statistical analysis with data collected from warehouse databases and regression analysis used to evaluate the impact of ICT on hardware, software, added value and labour productivity. The research suggests that investments in software influence added value and labor productivity more significantly than those in hardware. The analysis, based on data from 2020 to 2024, reveals that warehouses in Vilnius generate higher added value and labour productivity than other regions. In Kaunas, software contributed 25% to added value, while in Vilnius, it increased labour productivity by 1.97 times. These results emphasize the significant role of software in improving logistics performance and achieving a competitive advantage in logistics operations management.

Keywords: Human-Automation Integration, Knowledge management in production, Quality management
Abstract: Industry 5.0 (I5.0) is based on the perspectives of sustainability, cybersecurity, and human centricity. This study focuses on Lean 5.0 and aims to evaluate the impact of adopting Lean 5.0 concepts from the perspective of the Toyota Kata (TK) approach within an assembly line through empirical research. The methodological approach followed the TK cycles, including diagnosing the current process, classifying activities (value-adding vs non-value-adding), proposing solutions based on Industry 5.0 principles, and analysing the outcomes. A Likert scale questionnaire was also applied to measure employee perceptions, further validating the experiment's findings. The results of this study demonstrated that combining TK with digitised work instructions improved assembly processes by reducing bottlenecks, enhancing training, and decreasing reliance on expert operators. The proposed application and related practices can be utilised by organisations to support the Lean 5.0 implementation of the TK concept.

Keywords: Human-Automation Integration, Industry 4.0, Smart manufacturing systems
Abstract: In recent decades, technologies such as Automated Guided Vehicles (AGV) and Autonomous Mobile Robots (AMR) have emerged as promising solutions to assist or replace humans in physically demanding and risky tasks in logistics, improving worker safety and wellbeing. However, the integration of these advanced technologies with human activities poses challenges related to system design and task management, particularly in the context of Industry 5.0, which promotes human-centricity, advocating for sustainable systems that consider human needs and characteristics in smart manufacturing and logistics environments. This paper contributes to the Logistics 5.0 research stream by presenting an experimental study on the use of AMRs to assist operators in material handling tasks. The study, conducted in the SLIM laboratory at the University of Bergamo, evaluates operators' workloads during material handling activities with and without AMR assistance. The NASA-TLX questionnaire is employed to measure the perceived workload in the collaborative tasks. The results offer valuable insights into designing effective, human-centred logistics systems, enhancing operator efficiency and wellbeing.

Keywords: Decision-support for human operators, Facility planning and materials handling, Business Process Modeling
Abstract: Order picking remains a time-sensitive operation in warehousing, with pickers following predetermined routes. Previous research identified potential drivers for deviations from these routes through qualitative studies or descriptive data analysis. We take a novel approach by applying a statistical analysis on two years of data (i.e., 2 448 000 picks). Our mixed-effects logistic regression model shows that factors such as workload, picks completed, congestion and aisle layout may significantly affect the likelihood of route deviations. Such deviations could significantly reduce route efficiency. These insights highlight the need to integrate real-world dynamics into routing models, aiming to enhance overall efficiency in warehouse operations.

Keywords: Line Design and Balancing, Production planning and scheduling, Human-Automation Integration
Abstract: Human-robot collaboration (HRC) in assembly lines has emerged as an essential strategy and a growing trend in modern manufacturing. This paper presents a new mathematical model to optimize task scheduling and resource allocation in assembly systems with human and collaborative robots (i.e., cobots) and the presence of setup times. This model improves the efficiency and flexibility of assembly operations, reduces cycle time, increases the product's quality, and enhances human workers' well-being. The main objective of this model is to minimize cycle time by eliminating bottlenecks due to potential cobot downtimes. Supervised learning algorithms are trained to predict cobot availability using a dataset on operational hours, task complexity, and maintenance schedules. Among them, the XGBClassifier algorithm is selected with a high precision of 0.9312, a recall of 0.9240, and an F1 score of 0.9274. The results show how the operational budget impacts the cycle time and HRC.

Keywords: Heuristic and Metaheuristics, Line Design and Balancing, Design and reconfiguration of manufacturing systems
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Keywords: Line Design and Balancing, Design and reconfiguration of manufacturing systems, Optimisation Methods and Simulation Tools
Abstract: The construction industry is shifting towards off-site construction for improved labor conditions, better quality and less environmental impact. The prefabrication of timber framed houses contains of several unique wall-elements, which are assembled on one or more assembly lines and then put together on site. To stay competitive with the conventional construction industry the processes need to be improved. The aim of this study is to generate new improved task configurations for the assembly line and test them for real-life application. This is done by developing a mixed model assembly line balancing algorithm for prefabricated houses to create the configurations. Subsequently, these configurations are implemented in a validated discrete event simulation model to test for real-life feasibility and compare the results based on four indicators, cycle time per station, waiting time per station, time in system per wall-element and produced wall-elements, with the current production plant. The results show that the optimized task configurations generate more balanced cycle times, less waiting times and a time in system improvement for each wall element of around 20%. However, due to the complex and varying properties of the wall elements queuing effects can be observed with discrete event simulation and the outcome compared with the real-life production can only be improved by 1,8%. This research approach demonstrates the significant potential that the combination of optimization and simulation offers to operations managers in mixed-model planning within prefabricated housebuilding. However, it also reveals the necessity for an integrated procedure of assembly line balancing and sequencing to fully utilize these possibilities.

Keywords: Line Design and Balancing, Design and reconfiguration of manufacturing systems, Decision Support System
Abstract: To effectively address customization needs in building production is of importance to receive contracts in off-site construction, which consequently means that the number of variants to be handled in prefabrication lines increase. New approaches in off-site construction aim to better cover the need of customization by increasing the flexibility of mixed-model assembly lines for module production. However, this potentially increased flexibility in the prefabrication plans needs to be appropriately considered in various planning tasks at the tactical and operational level. Consequently, this paper addresses the balancing of high flexible mixed-model assembly lines, the sequence planning of modules, the employee deployment planning and material requirement planning. An algorithm for a planning tool was developed and tested to support these tasks to provide suitable plans for operations managers, giving them the ability to manage and control the productivity of high flexible mixed model assembly lines. The test results revealed the generation of suitable solutions from a line balancing and sequence planning perspective but showed the need for further research to enable the application of outcomes from employee deployment planning not just at the tactical but also at the operational level. Cycle times calculated for available projects show a line efficiency of up to 70% and propose worker station and task reallocation in order to improve worker’s utilization and the number of specific workers needed. This research consequently contributes to enable a higher level of customization of products off-site construction but at the same time facilitating a high level of productivity and efficiency of module prefabrication lines.

Keywords: Line Design and Balancing, Human-Automation Integration, Operations Research
Abstract: Disassembly is a crucial phase in remanufacturing and recycling, gaining increasing attention over the past decades due to its importance in sustainable resource management. While various decision-making challenges within the disassembly process have been studied, we focus on two closely related problems: the disassembly sequencing problem and the disassembly line balancing problem, specifically in the context of human-robot collaboration. We consider a bi-objective problem of total time-dependent cost minimization and workload balancing between activated workstations. The aim is to determine an optimal disassembly sequence that satisfies the problem’s objectives and assign tasks effectively to operators and workstations. To overcome these challenges, we formulate the problem as a mixed integer program and compare the ϵ-constraint and weighted sum methods to generate Pareto fronts. Computational experiments conducted on real-world end-of-life product structures validate the proposed model’s efficiency on small and medium-scale structures.

Keywords: Supply Chain Management, Simulation technologies, Modelling Supply Chain Dynamics
Abstract: While most research tends to examine resilience capabilities through the lens of a single strategy, supply chain management teams in practice often pursue integrated solutions that combine multiple strategies to achieve desired levels of resilience. Our study introduces an innovative two-layer digital supply chain twins (DSCTs) framework that connects the shop floor layer with the broader supply chain network layer. The DSCTs facilitate the simultaneous application of various resilience strategies. We then investigate the synergy of implementing six resilience strategies, which encompass operational and strategic levels, across different stages of disruption. The first four strategies are allocating available material inventory, activating backup suppliers, and deploying flexible on-demand resources such as labor and transportation. The other two strategies are strategic reserves for material substitution and repurposing. Combining six strategies into a unified decision-making framework allows us to assess the coevolution of decision processes and environmental conditions. Finally, we propose a set of structured experiments to find the best combination of strategies across various disruption profiles, considering supplier lead-time and supplier structural network characteristics. Our results include a framework for combining resilience strategies and a method to identify the best combination of such strategies—an essential component of any DSCTs solution.

Keywords: Supply chains and networks, Supply Chain Management
Abstract: The fast-developing global economy and increasingly occurring crises require multidisciplinary ap-proaches to supply chain resilience. Previous research on supply chain resilience has mainly focused on using either business or economics methods with only fragmented efforts of combining both. Through a combined factorial and discrete choice analysis, this study proposes a framework to evaluate proactive and reactive supply chain resilience measures and contributes to the knowledge of supply chain decision-making. In particular, we propose principles of a method to evaluate the overall attractiveness of resili-ence portfolios that include their costs and benefits. We provide some insights into the trade-offs supply chain managers might have to resolve between the different resilience measures, their benefits, and costs from a multi-stakeholder perspective.

Keywords: Supply chains and networks
Abstract: Efficiency, resilience, viability and sustainability of supply chains can impact not only performance of particular firms but also influence national and global economies. However, to date the supply chain research at macro-economic level is rather fragmented and characterized by quite isolate layers of business and economics. Currently, a trend to some integration of these two layers can be observed. For example, supply chain data are used for analysis of shock propagation through national and global economies as well as for achieving sustainability goals, cross-industry ripple effects become of increasing importance in light of global geopolitical uncertainties, and supply chain resilience analysis takes the lens of global ecosystem viability. These and many other examples of connecting business and macroeconomic perspectives of supply chains allow generalizing a novel research area: Supply Chain Economics. In this paper, we begin with outlining management and economics views on supply chains targeting to identify their commonalities and difficulties. We then classify the existing research on the interface of supply chain management and economics identifying future research areas.

Keywords: Supply chains and networks, Supply Chain Management, Industry 4.0
Abstract: We extend the recently developed intelligent digital twin (iDT) framework toward digital supply ecosystem notion. Digital ecosystem of a supply chain is a set of digital technologies, AI-based knowledge manage-ment systems, and data spaces and platforms enabling digital twins and simulation models. Digital twins are generated and adapted in digital ecosystems representing physical systems and objects in a data- and knowledge-driven manner. Specifically, we elaborate on how simulation, digital technologies and artificial intelligence (AI) can be combined to achieve a principally new quality of modeling and decision-making support in supply chain management. Digital ecosystems can be instrumental in building the capability for automatic generation and adaptation of models. That is, instead of having an expert construct a model based solely on their knowledge of the system, it involves enabling the system to build its model and, more importantly, adapt this model dynamically based on evolving knowledge (e.g., using a combination of on-tologies and generative AI) and constantly updated data. Moreover, ecosystems help not only in building models but also in automatically generating scenarios for modeling (e.g., disruption and crisis scenarios). Essentially, it encompasses a combination of observation (i.e., object identification) and control tasks. We contextualize the application of digital ecosystems using examples of supply chain resilience, ripple effect, and stress testing.

Keywords: Supply Chain Management, Supply chains and networks
Abstract: Lean principles such as just-in-time and single sourcing have been proven to increase supply chain efficiency. Multiple crises such as COVID-19 pandemic and global semiconductor shortages have revealed fragility of lean supply chains when facing disruptions. Most of the recent research on supply chain resilience suggests moving away from lean and building redundancies to cope with disruptions. While the redundancies such as extra inventory, backup sourcing, and capacity reservations can help protect the supply chains and ensure operations continuity in the presence of disruptions, they might adversely affect efficiency. In these settings, two important questions arise: (i) do lean and resilience contradict each other? and (ii) can lean and resilience complement each other? In other words, we are asking a question if a supply chain can be both efficient and resilient. In this paper, conceptual frameworks AURA (active usage of resilience assets) and LCN (Low-Certainty-Need Supply Chain) which articulated the concept of lean resilience are extended toward the notion of a tipping point between lean and resilient. We propose a formal model that can be used to identify a combination of lean and resilience measures in the supply chain.

Keywords: Decision-support for human operators, Monitoring, diagnosis and maintenance of manufacturing systems, Smart manufacturing systems
Abstract: Condition-based maintenance systems play a major role in the enduring effort for modern production facilities to stay competitive. Driven by the ongoing digitalization of manufacturing systems, the aim is to increase the utilization of machines and their components and avoid unexpected failures during operation. Anomaly detection algorithms are the basis of a prognostics and health management process, but so far, the deployment of such algorithms is associated with significant labor from experts. Among other things, experts have to select appropriate data, find the best algorithms and their hyper-parameters, evaluate the performance and deploy a trained model for the online production environment. This paper presents a semi-automatic framework for the deployment of anomaly detection algorithms for manufacturing systems. It consists of a user interface that offers step-by-step guidance through the process of data-, algorithm- and parameter setup, as well as evaluation and export of a trained model. The core of the system is an advanced meta-learning-based algorithm recommendation method. Furthermore, the paper presents a user-oriented architecture that integrates all the necessary components, its aspects of IT security and shows general acceptance by a user experience test. The meta-learning recommendation system shows a significant reduction of the mean of the F-score deficiency towards the optimal selection of 25% compared to a benchmark selection.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Optimization and Control, Heuristic and Metaheuristics
Abstract: A defect Lithium-ion battery defect detection is critical due to the widespread use of these batteries. Ensuring their safety and performance is essential, as defects can lead to serious issues, such as overheating and explosions. Early defect detection enhances battery reliability, extends lifespan, and reduces manufacturing costs by mitigating warranty claims and recalls. This paper presents a machine learning-based framework for detecting defects in lithium-ion batteries used in neon signs. Our approach combines various ensemble classification algorithms as base estimators for final stacking meta-learners. A Genetic Algorithm (GA) is used for feature selection, followed by model optimization using Bayesian hyperparameter tuning. Stacking methods with Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) as meta-learners are employed to enhance classification accuracy for defect detection in lithium-ion batteries used in neon panels. Our Hybrid Stacking-Bayesian (HSB) approach demonstrates the effectiveness of these models in accurately identifying defective batteries, contributing to improved manufacturing quality control and sustainability by minimizing waste and optimizing resource utilization. Implementing our model on real lithium-ion battery data showcases its potential for practical applications in the industry.

Keywords: Transportation Systems, Decision Support System, Modelling Supply Chain Dynamics
Abstract: Regional transport systems face unique challenges, such as low population density and limited infrastructure, complicating accurate demand estimation, yet essential to any approach to designing or managing transport activities. This paper introduces a demand estimation framework tailored to regional contexts, integrating both passenger and freight needs into a unified, utility-based model that dynamically adjusts to user expectations and transport mode characteristics over time. The model incorporates an original utility function that weights key transport features based on specific user profiles. An illustrative case allows validating the framework’s applicability, illustrating its ability to map demand flows, supporting it as an effective decision-support tool for regional transport operators.

Keywords: Optimization and Control, Modelling Supply Chain Dynamics, Supply Chain Management
Abstract: This research presents the development of a Serial-Closed-Loop-Multi-Stage Manufacturing System from a circular economy perspective. The system integrates a production line where five specialized machines operate across both forward and reverse remanufacturing processes. However, due to varying deterioration levels, these machines cannot consistently produce defect-free items, resulting in increased scrap and customer penalty costs. To address this challenge, we propose a mixed-integer linear programming (MILP) model that optimally allocates quality inspection stations and schedules for preventive maintenance across multiple working shifts. The objective is to minimize total production costs, including quality control, maintenance and customer penalties. The model’s effectiveness is validated through numerical experiments under three distinct machine deterioration scenarios, demonstrating its potential to enhance efficiency and sustainability in modern manufacturing systems.

Keywords: Simulation technologies, Supply Chain Management, Heuristic and Metaheuristics
Abstract: The rapid evolution of omnichannel retail demands innovative fulfillment solutions to meet dynamic customer expectations. This study presents a conceptual fulfillment model leveraging Deep Reinforcement Learning (DRL) to optimize decisions across inventory management, order allocation, and delivery logistics. Simulations of a medium-sized retailer's operations over a year reveal the DRL model's adaptability, achieving a higher profit compared to traditional heuristics. While results underline DRL's potential in dynamic environments, further empirical validation with real-world data is essential to ensure robustness and scalability across diverse operational contexts, in addition to applying comparisons with metaheuristics. This research advances the integration of AI in supply chain decision-making.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The industry is increasingly using data-driven predictive maintenance with machine learning to prevent equipment failures. However, manufacturing systems are inherently complex and operate in dynamic, non-stationary environments, leading to the phenomenon of concept drift. Due to edge device limitations, traditional full retraining methods are often impractical, making incremental learning a potential alternative. This study evaluates both approaches through simulated real-world scenarios. Results show that full retraining generally performs better, especially in ideal and degraded conditions. While incremental learning shows acceptable performance with accurate labels and imbalanced datasets, it struggles when dealing with significant labelling inaccuracies.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Operations Research, Sustainable Manufacturing
Abstract: Maintenance plans for production assets must balance the cost of maintenance against failures/downtime penalties. For marine renewable energy (MRE) generators, this trade-off can be achieved by minimizing the levelized cost of energy (LCOE) which accounts for costs from setup, decommissioning, and operations and maintenance. Thus, the present paper details a novel optimization formulation to minimize LCOE for an MRE undergoing selective maintenance (SM) either offshore or onshore. A binary integer programming formulation is proposed with offshore maintenance likelihood captured via an exogenous parameter. Numerical experiments involving a single MRE system are tested to compare the LCOE model against classical formulations using minimum-cost and maximum-reliability objective functions. The results demonstrate the benefits of minimizing LCOE in comparison to other objective functions when balancing competing priorities and desiring more flexible/nuanced maintenance plans.

Keywords: Sustainable Manufacturing, Production planning and scheduling, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: This study focuses on addressing the growing scarcity of natural resources by exploring strategies for conservation and recycling in energy systems. With increasing pressure on energy supplies and environmental sustainability, the recovery and reuse of waste heat present viable solutions to reduce reliance on traditional resources. This approach emphasizes sustainable development by rethinking energy use in residential heating systems, promoting efficient utilization, and minimizing waste. The results indicate a significant decrease in auxiliary heating consumption, replaced by recovered heating. By integrating heat recovery systems, the study highlights the potential for reducing energy consumption and contributing to a more sustainable and resource-conscious future.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Industry 4.0, Simulation technologies
Abstract: Industry 4.0 technologies, including digital twin, have the potential to deal with maintenance challenges within SMEs. This paper investigates practical solutions to implement Maintenance 4.0 using digital twin. The proposed framework leverages low-cost sensors, subscription-based software, cloud computing, a digital twin system, and blockchain technology to create an affordable and secure solution. By integrating real-time monitoring, machine learning algorithms, and remote diagnostics, the framework empowers SMEs to optimize their maintenance processes, enhance equipment reliability, and improve overall productivity. The viability of the proposed framework is evaluated using a real case. The results show the advantage of the framework compared to other alternatives in terms of reliability, availability, maintainability, sustainability, and security. The paper concludes by discussing future research directions in this domain.

Keywords: Supply Chain Management, Inventory control, production planning and scheduling
Abstract: Effective forecasting of intermittent demand is critical for optimizing spare part inventory management due to its sporadic and erratic nature. This study assesses and compares various forecasting methods tailored for such demand patterns, including Croston’s method, exponential smoothing, ARIMA, neural networks, Facebook Prophet, Syntetos-Boylan Approximation, and moving average models. Historical demand data from a real-world supply chain is analyzed to evaluate accuracy and bias, identifying the most suitable model for practical applications. The findings provide valuable insights for improving inventory planning, minimizing costs, and enhancing supply chain efficiency, contributing to both academic knowledge and practical advancements in spare part management.

Keywords: Operations Research, Supply chains and networks, Optimisation Methods and Simulation Tools
Abstract: Prototype parts logistics in the automotive industry is characterized by unique parts and logistics networks with different warehouse locations. The future assembly location of the prototype parts is unknown. However, as soon as the assembly location is known, the prototype parts must be delivered to the customer at short notice. Weckenborg et al. (2024) have developed a hybrid transshipment model for these characteristics in a hub and spoke network. In the model reactive transshipments will be used to transship other unique parts proactive to different warehouses. For the decision on proactive transshipments, approximated future shipping costs are introduced for each part in each warehouse. The approximated future shipping costs are calculated based on the fixed transportation costs and the demand probabilities. In the following numerical study, the influence of different demand probability distributions and the switch from a hub and spoke network to direct deliveries to the various locations on the hybrid approach is analyzed. The study shows that a demand probability distribution with a high demand probability for one customer can reduce transportation costs. The results of the numerical study also show that the number of proactive transshipped parts can be drastically reduced if parts are delivered directly from the supplier to the warehouse with the lowest approximated future shipping costs depending on the demand probabilities.

Keywords: Supply chains and networks, Industry 4.0
Abstract: This paper explores the potential of Internet of Things (IoT) technologies, such as artificial intelligence (AI), big data analytics, and cloud computing in enhancing waste collection and processing operations. A short literature review is conducted to identify various applications of these technologies in optimizing waste management processes, including real-time data collection, automated monitoring, predictive maintenance, and dynamic route optimization. Based on these findings, a novel approach is proposed that integrates all stakeholders in the waste management value chain through a cloud-based waste management platform. This platform facilitates seamless data sharing and communication, enabling coordinated real-time decision-making and efficient resource allocation. The conceptual model developed in this study is used as a foundation for creating a mathematical model that supports real-time optimization of routing, scheduling, and assignment tasks for waste collection services. The model aims to dynamically adjust operations in response to changing conditions, such as waste volume fluctuations and traffic patterns. This optimization framework enhances key performance indicators (KPIs) by reducing operational costs, minimizing environmental impact through lower emissions, and improving service reliability and customer satisfaction. The proposed approach represents a significant advancement in smart waste management, driving sustainability and efficiency through the use of cutting-edge digital technologies.Copyright © 2025 IFAC

Keywords: Facility planning and materials handling, Operations Research
Abstract: Effective waste collection, particularly selective waste collection, plays a crucial role in advancing sustainable development and protecting the environment. Unfortunately, proper waste separation is often neglected by individuals and organizations, leading to increased pollution and resource depletion. Large institutions like schools, offices, and factories face similar obstacles, often due to inadequate infrastructure for efficient waste bin placement. To address this issue, we developed an algorithm designed to optimize bin placement, addressing a Facility Location Problem, modeled as a graph-based task. Our approach identifies ideal bin locations to maximize efficiency and accessibility. We tested and validated the algorithm using data from a university campus, specifically implementing it at the University of Miskolc. This optimized layout not only promotes proper waste segregation but also encourages the university community to engage actively in sustainable waste practices.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Line Design and Balancing, Production planning and scheduling
Abstract: Balancing assembly lines has drawn significant research interest over decades due to its vital impact on throughput without adding extra resources. This paper targets production facilities operating multiple identical assembly lines, particularly those facing challenges due to variable task durations across workstations. We propose a methodology for layout planning that prioritizes system-wide balance over independent line balance, leveraging simulation techniques. Our methodology involves removing workstations with long task durations from individual lines and centralizing them to serve all lines. In a real-world case study, the proposed methodology boosts throughput by up to 12% by optimizing the utilization of existing resources.

Keywords: Modelling Supply Chain Dynamics, Supply chains and networks, Decision Support System
Abstract: Supply chain management faces increasing challenges due to growing interactions and uncertainties, requiring advanced knowledge management approaches. Supply Chain 5.0 emphasizes sustainability, resilience and human-machine collaboration, consequently needs innovative approaches. To address the traditional systems limitations, the reconfigurable supply chain has emerged, which requires dynamic knowledge management solutions. This paper proposes an ontological framework to support supply chain reconfiguration using semantic modeling through ontology. Ontologies formalize, organize and integrate complex knowledge, improving decision making and ensuring interoperability between systems. The framework incorporates key performance indicators to enable real-time adaptability and maintain optimal performance. By bridging traditional methods with reconfigurable systems, it addresses critical knowledge management gaps while aligning with Industry 5.0 principles. A comparative evaluation underlines the novelty of the framework and its potential as a valuable tool for managing dynamic, sustainable and resilient supply chains.

Keywords: Optimisation Methods and Simulation Tools, Modelling Supply Chain Dynamics, Sustainable Manufacturing
Abstract: The European Union's ambitious carbon reduction targets for 2030 demand innovative solutions across energy-intensive sectors. Among these, the glass industry is actively seeking sustainable alternatives to natural gas combustion, a major source of carbon emissions in glass production. This paper investigates the potential of hydrogen as a decarbonization pathway for the glass industry, focusing on a use-case from a glass manufacturing facility in Europe. To address these emissions, this study explores the replacement of conventional fuels with hydrogen, a promising clean fuel alternative. Hydrogen not only offers a pathway to substantial carbon reduction but also aligns well with the glass industry's operational requirements for high temperature processes. However, the transition requires a strategic approach to the hydrogen value chain, ensuring an optimized supply, storage, and distribution network. This paper discusses the techno-economic and environmental aspects of hydrogen integration in glass manufacturing, emphasizing hydrogen production, delivery, and utilization frameworks that enhance cost-effectiveness and sustainability in the value chain. Through this lens, we aim to provide a viable roadmap for hydrogen adoption within the glass industry, offering insights into the broader implications for sectoral decarbonization across Europe.

Keywords: Sustainable Manufacturing, Modeling, simulation, control and monitoring of manufacturing processes, Inventory control, production planning and scheduling
Abstract: Hydrogen is expected to be critical in decarbonizing energy-intensive industries, including the glass sector. Replacing natural gas with hydrogen as a combustion fuel in the melting process offers significant environmental advantages by eliminating direct CO₂ emissions. However, hydrogen adoption faces challenges, including the lack of hydrogen-related infrastructure in glass manufacturing plants. This paper introduces an approach for modeling the hydrogen supply to the glass melting furnace to help glass manufacturers evaluate the economic and environmental impacts and the required infrastructure for this transition. The study explores on-site hydrogen production via water electrolysis and external hydrogen supply by trucks and demonstrates its application through a case study from the glass industry. The model relies solely on long-term aggregated data, making it easily applicable and modifiable. While hydrogen integration reduces direct CO2 emissions, results show that its overall impact depends on its production method. Additionally, policy incentives and electricity sources strongly influence its economic viability.

Keywords: Operations Research, Supply Chain Management, Optimisation Methods and Simulation Tools
Abstract: This study presents a multi-objective MILP model to design a resilient closed-loop supply chain (CLSC) for glass production, integrating green hydrogen (GH) and natural gas (NG) as fuels for furnaces. The model optimizes batch compositions, leveraging recycled glass (cullet) to minimize costs, energy use, and CO2/NOx emissions. A Pareto frontier reveals trade-offs between objectives using an LP-metric optimization framework. A scenario-based stochastic technique manages uncertainties in demand and recycling rates, while a stochastic ρ-robust approach enhances supply chain resilience (SCR) by mitigating batch supplier disruptions. Results identify three solution clusters: energy- and emission-focused, cost-dominant, and balanced trade-offs.

Keywords: Scheduling, Sustainable Manufacturing, Industrial and applied mathematics for production
Abstract: Inspired by a real-life manufacturing case study, this paper addresses the Integrated Scheduling and Energy Management (ISEM) problem. This problem consists of scheduling jobs in an heterogeneous hybrid flowshop where the machines in some stages process the jobs in batches, as well as managing the energy consumption provided by an Industrial Microgrid (IMG). The IMG includes the generation of energy using renewable Sources, a power plant operating using gas purchased to an external provider, and a Battery Energy Storage System. The objective is to minimize the operating costs of the IMG while scheduling the jobs within the time allocated. A Mixed Integer Linear Programming (MILP) model for this rather complex problem is provided, and computational experiments are carried out, showing that the MILP model is able to reach the optimal solution (or with a minimal optimality gap) for realistic cases, even if the number of stages and jobs that the model can handle within a reasonable computational effort is limited.

Keywords: Decision Support System, Sustainable Manufacturing
Abstract: The development of sustainable building materials has led to increased interest in geopolymer (GP) science for the possibility of incorporating a great number of industrial wastes and by-products, such as the Red Mud (RM). This study focuses on the selection of the optimal RM-based GP formulation among a range of variants developed in laboratory with different RM content. To determine the best mix design, the VIKOR method, a multi-criteria decision-making (MCDM) technique, was employed. This method allows the selection of a compromise solution that balances technical, physical, and environmental criteria across the different formulations. The criteria were selected and weighted following a consultation with a panel of experts in this field. The results indicate that the VIKOR method effectively identified the optimal GP formulation, which exhibited a superior balance of mechanical and physical performance. The selected formulation might be produced at industrial scale, representing a viable alternative to traditional cementitious binders, offering both enhanced sustainability and performance for construction applications.

Keywords: Sustainable Manufacturing, Supply Chain Management, Pricing and outsourcing
Abstract: We investigate how ESG (Environmental, Social and Governance) initiatives can serve as an effective incentive within the e-waste recycling market. The decision problem is formulated as a game in which a manufacturer and a recycler decide whether to implement ESG initiatives, and then set the new product price and recycling price respectively. There are four possible equilibria based on the implementation of ESG initiatives: both the manufacturer and recycler implement ESG initiatives, only the manufacturer implements ESG initiatives, only the recycler implements ESG initiatives, or neither implements ESG initiatives. Our findings indicate that the manufacturer's environmental penalty factor increases the price of new products and reduces their quantity, negatively impacting the profits of both the manufacturer and the recycler. In contrast, the recycler's penalty factor does not impact the manufacturer's profit. The optimal strategy is easy to execute, since the two players need only consider their respective environmental footprint thresholds. Such thresholds are also dependent on consumers' preferences for ESG initiatives. ESG initiatives are effective, particularly when the environmental footprint of manufacturing and recycling is low. Additionally, setting moderate tariffs can help balance recycling levels and maintain profitability, and simpler supply chain structures are more conducive to achieving ESG objectives. Lastly, the smaller the value of e-waste is, the recycler will choose not to implement ESG initiatives. These insights provide useful guidelines to optimize ESG initiatives implementation in the e-waste recycling market.

Keywords: Supply Chain Management, Decision Support System, Optimisation Methods and Simulation Tools
Abstract: Due to the value of resource recovery and the development of a circular economy, resource recycling has gathered global attention. Resource recycling management has been one of the primary challenges of urban development. Incentive-based recycling systems have been used worldwide to increase the willingness of residents to take part in resource recycling. With a series of studies, this discussion contribution focuses on behaviour analysis, system design and inventory-routing optimisation for incentive-based recycling. Modelling approaches including partial least squares structural equation modelling, Internet of Things (IoT) and big-data analytics, and two-stage dual-objective multi-period stochastic programming were designed to improve incentive-based recycling. Real-world cases and data from Shanghai demonstrated the effectiveness of such models and the performance of the improved incentive-based recycling system.

Keywords: Pricing and outsourcing, Optimization and Control, Inventory control, production planning and scheduling
Abstract: This literature review explores the implementation of dynamic pricing (DP) in Small and Medium-sized Manufacturing Enterprises (SMEs) navigating a competitive market and facing a VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) environment. We assess how this strategy could affect their profitability and competitiveness, particularly for those differentiating through customized product offerings. Our methodology involves a detailed analysis of 28 models suited to the manufacturing context, revealing a predominant focus on capacity management rather than integration and minimization of overall productions costs. Our findings suggest an underutilized potential of DP, combined with production management, as leverage for agility in these enterprises. The study highlights the need for models more aligned with SME operational realities and opens avenues for future research, especially through practical cases and implementation cost evaluations.

Keywords: Smart transportation, Operations Research, Scheduling
Abstract: This paper addresses the complex multi-period log-truck routing and scheduling problem (LTRSP) in the forest industry, proposing an enhanced mathematical programming formulation and a decomposition heuristic to solve large-scale instances. The Canadian forest industry faces significant logistical challenges due to vast distances, seasonal variability, market fluctuations, and environmental concerns. Efficient transportation is essential for maintaining both economic viability and environmental sustainability. This research presents a comprehensive framework for routing and scheduling, starting with an analysis of industry rules to design a routing network. A mixed-integer linear programming (MILP) model is then formulated to capture these rules, integrating spatial and temporal constraints. Subsequently, a solving approach, Relax-and-Fix, is applied to historical data provided by a Canadian forest company. The results demonstrate that the framework can generate optimal solutions for daily problems and near-optimal solutions for weekly problems within reasonable computation times. This work offers an end-to-end framework for tackling LTRSP, developed in collaboration with forest companies and incorporating all their critical business constraints, distinguishing it from existing approaches in the literature.

Keywords: Industry 4.0, Supply Chain Management, Sustainable Manufacturing
Abstract: This study explores the intersection of artificial intelligence (AI), circular economy (CE), and Industry 5.0 (I5.0) by analyzing research trends through a text-based examination of the titles, keywords, and abstracts of 422 scholarly articles indexed in the Web of Science database. Using advanced text mining and natural language processing techniques, we identify dominant themes, emerging topics, and critical gaps in literature. The analysis highlights key applications of AI to improve CE practices, such as supply chain resilience, waste management, and sustainable manufacturing, within the human-centered and sustainable framework of I5.0. Key findings include the growing importance of Industry 4.0 and big data analytics as research hotspots, as well as the integration of AI-driven technologies into CE innovations. This research contributes to the understanding of how AI integrates with CE and I5.0, and provides valuable insights for researchers, policymakers, and practitioners aiming to promote sustainable and resilient industrial transformations.

Keywords: Industry 4.0, Sustainable Manufacturing, Supply chains and networks
Abstract: This study investigates the impact of sustainable supply chain design on Industry 4.0 in Egyptian manufacturing through quantitative analysis of 368 responses across various industries. Using Confirmatory Factor Analysis and Structural Equation Modeling, the research examines key embrace and constraint factors through the sixth bottom line perspective. This approach provides a comprehensive bidirectional framework that advances traditional triple bottom line approach by systematically analyzing both embrace and constraint factors across all sustainability dimensions. The study demonstrates that the Egyptian industry is sufficiently aware of the compatibility between sustainability and the use of Industry 4.0. However, the transition to Industry 5.0 may encounter certain challenges in the Egyptian context. Keywords: Industry 4.0, Industry 5.0, Sustainable Supply chain design, Egypt, Sixth bottom line.

Keywords: Modelling Supply Chain Dynamics, Supply Chain Management, Sustainable Manufacturing
Abstract: The circular production approach, based on the continuous recovery of value from waste as raw materials for manufacturing processes, is gaining increasing prominence in companies worldwide. One of the practices that effectively embodies the principles of the Circular Economy is Industrial Symbiosis. The aim of this work is to compare different replenishment policies – push and pull – in the Symbiotic Supply Chain archetype. The results reveal that the pull policies offer more balanced performance across multiple indicators preserving consistent dynamic performance and efficient inventory management, also for high volume of waste.

Keywords: Sustainable Manufacturing, Industry 4.0, Supply chains and networks
Abstract: Today, twin transition- synergy between sustainability and digital transformation, has become a strategic imperative for organizations. However, integrating these agendas presents significant challenges. This paper proposes a conceptual framework that combines dynamic capabilities theory and stakeholder theory to guide organizations in achieving twin transition. The framework emphasizes the development of sensing, seizing, and transforming capabilities within key strategic areas, including leadership, culture, ecosystem, products, operations, and technology. Stakeholder engagement ensures accountability, transparency, and a shared commitment to twin transformation goals throughout each stage. The proposed framework advances management literature by providing a structured model for twin transformation. By balancing organizational adaptability with stakeholder expectations, organizations can build long-term value and competitive advantage through an integrated approach to digitalization and sustainability.

Keywords: Supply Chain Management, Supply chains and networks
Abstract: The construction industry is one of the most polluting on a global scale. The dominant logic in the industry is linear: new buildings are built while old ones are demolished, and rarely their materials are recycled or reused. Moving towards circular supply chains can reduce the environmental impact of the sector. To enable the transition to circular supply chains in construction, there is the need to shift to new design paradigms, among others Design for Disassembly. The study focuses on this design approach, which seeks to maximize end-of-life material reuse or recycling by separation of components. In this paper, we analyze the case of the architecture of textiles. Throughout interviews with experts and the analysis of selected cases from secondary sources, an assessment is carried out to establish how suitable these constructions and their materials are in relation to Design for Disassembly.

Keywords: Supply chains and networks, Industry 4.0, Operations Research
Abstract: This study focuses on fairness and latency issues in blockchain-based supply chains. To address these challenges, a novel consensus framework is developed to ensure a decentralized and efficient transaction validation process. This framework consists of three mathematical models designed to optimize global payoff, payoff allocation, and transaction processing time within a selected network of validators. A reinforcement learning technique is applied to address the inherent complexity of the transaction-scheduling problem. Simulation results demonstrate the effectiveness of the proposed model by balancing resource utilization, ensuring fair payoff distribution, and reducing processing times. These findings highlight the potential of this approach for developing decentralized and scalable blockchain-based supply chain systems.

Keywords: Supply chains and networks, Supply Chain Management, Risk Management
Abstract: Agri-food Supply Chains (AFSCs) face increasing disruptions from natural disasters, pandemics, and economic crises, necessitating robust quantitative analysis for resilience. This study conducts a Systematic Literature Review (SLR) using text mining and Latent Dirichlet Allocation (LDA) to identify six key research themes, including risk management, pandemic effects, simulation-based resilience, climate change, market price volatility, and optimization models. Findings reveal that multi-criteria decision-making, simulation, optimization, and machine learning are widely used, yet gaps remain in Artificial Intelligence (AI)-driven risk prediction, real-time data integration, and adaptive decision-making. This review offers insights for researchers and practitioners, emphasizing the need for AI, digital twins, and blockchain to enhance AFSC resilience.

Keywords: Supply Chain Management, Quality management, Risk Management
Abstract: This paper studies the impact of blockchain-enabled traceability systems on the cascade risk in agricultural supply chains using a game-theoretic approach. Our analysis considers four scenarios based on the adoption of a traceability system and the implementation of quality testing. We find that collectors and sellers can benefit from higher profits with blockchain adoption. However, farmers can benefit from the traceability system by providing lower-quality products if the collector does not take the quality testing.

Keywords: Supply Chain Management, Industry 4.0, Sustainable Manufacturing
Abstract: The seafood industry faces growing sustainability challenges, including overfishing, resource inefficiency, and environmental degradation, necessitating innovative production alternatives. While traditional SCs benefit from established infrastructure and consumer trust, their high resource demand and operational inefficiencies highlight the need for sustainable alternatives. This study compares traditional and 3D-printed salmon SCs, using process mapping by flowcharting and sustainability metrics to evaluate their environmental, economic, and social impacts. Findings indicate that 3D-printed salmon reduces carbon emissions by up to 86% and freshwater consumption by 95%, primarily by eliminating farming, feed production, and long-distance cold storage. Additionally, localized production lowers logistical costs and enhances resource efficiency. Despite challenges related to consumer acceptance, regulatory approval, and scalability, 3D printing presents a promising complement to aquaculture, supporting long-term sustainability in seafood production.

Keywords: Inventory control, production planning and scheduling, Supply chains and networks, Design and reconfiguration of manufacturing systems
Abstract: This study examines the integration of additive manufacturing (AM), specifically 3D printing (3DP), into retail supply chains to optimize inventory costs while maintaining high service levels (CSL ≥ 95%). A dual-sourcing inventory model is developed, balancing demand between traditional suppliers and in-house 3DP production. The model, solved using Microsoft Excel Solver, incorporates economic order quantity (EOQ), economic production quantity (EPQ), and reorder points to minimize total costs. Experimental results show that hybrid sourcing with 3DP reduces inventory costs, particularly at higher demand levels, while capacity constraints limit full adoption. Findings suggest that retailers should invest in AM expansion to maximize cost efficiency. This study provides a data-driven framework for hybrid inventory strategies and highlights future research directions in demand uncertainty, queueing effects, and advanced optimization techniques.

Keywords: Design and reconfiguration of manufacturing systems, Supply chains and networks, Modelling Supply Chain Dynamics
Abstract: The need for effective absorptive, adaptive, restorative and transformative strategies to build resilience with enhanced supply network performance has been identified as a critical research need, as many of the available strategies tend to be organization-oriented and often require substantial investment, highlighting the need for more cost-effective, network-wide approaches. Resilience in supply chains refers to their ability to anticipate, absorb and recover from disruptions while maintaining core functions and adapting to emerging challenges. An emerging proposition reported in the literature to enhance supply network resilience is reconfigurable supply networks. The networks are characterized by their modularity, flexibility and adaptability, which help them dynamically adjust the structure, processes and resources in responding to changes in the external environment. To advance the notion of reconfigurable supply networks, this paper proposes a multi-agent optimisation model. As part of this model, auctioning-based communication and coordination protocols are implemented to drive organisational decisions and achieve the expected supply network level performance. A refrigerator supply network is used to evaluate the model under baseline conditions, which are then subject to scenario analysis to account for supply network conditions. The results demonstrated the effectiveness of the proposed model in making supply network configuration decisions that help in multiple ways to build supply network resilience.

Keywords: Supply Chain Management, Supply chains and networks
Abstract: Short food supply chains have increasingly appeared as sustainable alternatives to the traditional food system, though their sustainability lacks scientific consensus. This systematic literature review examines the indicators used to evaluate the sustainability of short food supply chains, detailed through the ``Sustainability Assessment of Food and Agriculture systems'' framework. The analysis reveals a focus on quantitative evaluations, mainly in occidental countries, with an emphasis on farmers and supply chain configurations with maximum one intermediary. The economic and environmental pillars are the most assessed ones while some social themes are less studied. This review highlights the need to suggest a comprehensive indicator framework tailored to the needs and constraints of the stakeholders of short food supply chains.

Keywords: Modelling Supply Chain Dynamics, Supply chains and networks, Sustainable Manufacturing
Abstract: Food waste represents a significant challenge for the food sector, and circular economy practices can reduce the negative sustainability impacts of food waste. Nevertheless, the Circular Economy Rebound (CER) effect may diminish the effectiveness of circular practices. To explore CER in the food sector and the circumstances that trigger it, this study develops an agent-based model simulating consumer behavior in response to circular economy strategies. This method provides valuable insights into the potential impacts of circular initiatives, also offering guidance for companies aiming to adopt circular economy practices effectively.

Keywords: Supply chains and networks, Operations Research
Abstract: This research reflects a decade's worth of intervention-based research on utilizing alternative supply chain provisions (ASCP) to combat food poverty within local communities in the UK. This research is intervention-based longitudinal research, deploying a community Operational Research (COR) approach. The main contribution lies in the development of a theoretical house model, based on insights learned from a decade of intervention research, which articulates the effective mechanisms for ASCPs to tackle food poverty.

Keywords: Decision Support System, Supply chains and networks, Optimisation Methods and Simulation Tools
Abstract: Embracing reusable and sustainable packaging is an opportunity to reduce the environmental burdens across agri-food supply chains in alignment with the European Green Deal goals. This paper introduces a visual dashboard as a decision-support tool for Reusable Packaging Circular Supply Chains (RPSCs) within the agri-food sector. The dashboard integrates key operational parameters as levers, including material composition, production processes, reuse cycles, and logistical network configurations. The dashboard enables stakeholders to assess environmental and economic trade-offs in real time. As a computerized-aided design tool, the developed interface relies on data architecture and scripts hidden from the final users. A proof-of-concept application within an international food retailer's logistics network demonstrates the tool's practical utility, confirming the reductions in production and disposal emissions of reusable packaging compared to single-use. However, challenges persist in adopting reusable packaging, including a significant reliance on the logistical infrastructure, the impact of washing processes, and the enhanced costs linked to increased complexity. This paper underscores the importance of accessible operative tools in facilitating cross-functional collaboration toward a shared common goal, contributing to a more sustainable agri-food sector.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Design and reconfiguration of manufacturing systems, Decision Support System
Abstract: This study introduces a flow-generator platform designed to enable data-driven approaches, e.g., models, decision support systems, and digital twins, in production and storage systems. It aims to address the challenges of providing realistic operational data when actual data are unavailable as in greenfield scenarios. The platform simulates inbound and outbound material flows by capturing variability in production rates, shipping schedules, and storage processes. Users configure parameters such as production line capacity, items diversity, batch sizes, and material handling strategies through a visual dashboard, facilitating detailed analysis of operational production peaks, trends, and variabilities. By enhancing decision-making processes through robust and realistic data, the platform complements simulators, digital twins, and advanced modeling methods. A numerical example set in the food processing industry demonstrates its applicability, showcasing its ability to support robust performance modeling and optimization tailored to dynamic supply chain environments.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Sustainable Manufacturing, Decision Support System
Abstract: Driven by the need for dynamic decision making in disassembly, this contribution presents a solution for integrating real-time data into a digital twin for reactive disassembly planning. This integration enables reactive disassembly planning to adapt disassembly plans based on real-time data and to communicate such adaptations to the disassembly system. First, a system architecture for the integration is derived from the literature. Four tasks to be performed by the system are identified, namely the input data collection, input data structuring, plan adaptation, and plan communication. Corresponding system elements are designed, including a data structure for storing the real-time system data and for communicating plan adaptations. A morphological analysis of available technologies is presented to assist in the implementation. Finally, an exemplary system configuration derived from the morphological analysis is described, which was implemented and practically applied. The results enable future research in data-driven reactive disassembly planning and real-time data analysis of disassembly systems.

Keywords: Robustness analysis, Industry 4.0, Robotics in manufacturing
Abstract: Increasing the autonomy of the robots used for nuclear decommissioning has been the subject of further research recently. Hydraulic manipulators are valuable assets in the nuclear industry as they are powerful and contain very few electronic components. However, due to the high nonlinearity, designing a precise tracking controller is challenging. In this paper, a novel backstepping terminal integral sliding mode controller supported with an extended state observer (BTISMCESO) is designed to address this issue in the presence of external disturbances and uncertainties. To address the uncertainties in the actuator dynamic and to manage the complexity of the design a virtual control approach relying on the backstepping technique is applied in the proposed method. The lack of pressure sensors in the actuator dynamic is compensated by designing an extended state observer estimating the internal pressure of the piston. The stability of the closed-loop system in the presence of uncertainties is proved analytically using Lyapunov stability theory. An experimentally validated numerical model of the robot is used to evaluate the performance of the closed-loop system despite parametric uncertainties and external disturbances.

Keywords: Optimization and Control, Robustness analysis, Industry 4.0
Abstract: The autonomous system has shown several benefits when deployed in hazardous and unknown environments, however, the practical implementation of such systems faces various challenges due to uncertainties caused by the environment. Quadcopters are proven to be a useful robot for such environments with the ability to carry cameras and LiDAR and explore unknown environments. Nonlinear robust and adaptive control strategies are effective in controlling quadcopters against parametric and dynamic uncertainties as well as external disturbances in a hazardous environment. Although there are several advanced nonlinear control designs in the literature for quadcopter trajectory tracking, they are limited to the numerical simulations. In this study, four nonlinear robust flight controllers have been evaluated and compared on a real quadcopter in an experimental setting. The parameters of the quadcopter used in the controller design were measured from the quadcopter available in our lab. Furthermore, the controllers have been validated using the hardware in the loop from the PX-built Matlab package with the Pixhawk autopilot. These four controllers are compared on the basis of their control effort and power consumption, flight duration, memory usage, and integral absolute tracking error in front of the inertial parametric and dynamic uncertainties.

Keywords: Robotics in manufacturing, Smart manufacturing systems, Industry 4.0
Abstract: Recent advancements in robotic welding and vision-aided automation have transformed industrial processes by providing high precision, rapid detection, and adaptability compared to traditional methods. This study introduces a trajectory generation approach based on Deep Iterative Matching, leveraging RGB-D data from industrial cameras (Zivid 2) to calculate welding paths, initial positions, and correct placement errors. A real dataset of aluminum workpieces was created to train deep neural networks (DNNs) to handle measurement corruption caused by reflections. By integrating intelligent solutions, this research aims to improve automation reliability, repeatability, and efficiency, advancing welding technologies in complex industrial environments.

Keywords: Optimization and Control, Smart transportation, Robotics in manufacturing
Abstract: Library operations management aims to effectively coordinate resources to ensure timely delivery of materials and services to patrons. A critical component of this process is material handling (MH), which is pivotal in patron satisfaction and operational performance. To that essence, and through a specific focus on the moving processes of books, the primary objective of this study is improving operational efficiencies in connection with book availability for patrons, reducing the manual workload for librarians, and lowering operational costs. In this context, digital solutions for enhancing library material handling have been explored, with Autonomous Mobile Robots (AMRs) emerging as the most promising technology for library operations. Given the complexity of the library layout, optimizing material movement between locations using AMRs requires an analysis of layout strategies (centralized vs. decentralized) and a determination of the appropriate number of temporary buffers. Therefore, an optimization model was developed to minimize the costs and time associated with book-handling processes, offering a potentially scalable solution for broader library systems. The model was tested in the busiest section of Trondheim Public Library, Norway, and the results revealed significant benefits including increased automation and a more flexible temporary shelving wihin a decentralized layout.

Keywords: Decision Support System, Heuristic and Metaheuristics, Operations Research

Keywords: Inventory control, production planning and scheduling, Quality management, Operations Research
Abstract: This paper presents an economic order quantity (EOQ) model for a product with a deterministic shelf-life, after which it becomes unfit for use. An order is placed as soon as the inventory level reaches the reordered point, and the ordered lot arrives in stock after a positive, deterministic lead time. While demand is deterministic, the actual quantity received in inventory does not necessarily match the ordered amount. In particular, the received quantity – the yield – is random. A cost model is developed, and an optimization problem is formulated. Finally, numerical experiments are conducted to analyze system behavior.

Keywords: Pricing and outsourcing, Operations Research, Risk Management

Keywords: Inventory control, production planning and scheduling, Supply Chain Management
Abstract: Managing perishable supply chains is a complex task that needs a precise knowledge of the current inventory level. Using a high-accuracy sensor equipment is the most intuitive and applied method. The related inconveniences are the large economic cost and the susceptibility to cyber attacks, like, e.g., in the case of radio-frequency identification technology. Moreover, even the most precise of physically realized sensors always provides measures affected by a certain amount of error. In this paper we show that it is possible to obtain a very accurate inventory information using a cheap sensor equipment. The proposed solution consists of the series connection of an estimator with a cheap physical sensor. The estimator exploits the noisy measurements provided by the sensor to monotonically increase the accuracy of the inventory estimate independently of the source and amount of the noise affecting the inventory measures.

Keywords: Inventory control, production planning and scheduling, Supply Chain Management, Production planning and scheduling
Abstract: Managing perishable inventory in two-echelon supply chains is complex and requires efficient inventory management systems to preserve product quality, reduce product waste, and, subsequently, costs. This study introduces a novel Economic Order Quantity (EOQ) model for deteriorating items that optimizes the replenishment policy. The model incorporates time- and environment-dependent deterioration functions to address microbial decomposition and oxidation losses in a biomass-to-biofuel supply chain. Mathematical modeling and numerical analysis integrate microbial growth kinetics, transportation times, and cooling costs. The findings reveal that deterioration significantly impacts inventory levels and supply chain costs, with faster rates leading to more losses and expenses. The proposed model extends classical EOQ methods by dynamically adjusting order quantities and optimizing storage conditions. This study offers valuable insights to enhance supply chain resilience and sustainability.

Keywords: Modelling Supply Chain Dynamics, Operations Research, Optimisation Methods and Simulation Tools
Abstract: This paper discusses the challenge of production planning in a dyadic supply chain, where uncertainties disrupt plans that are updated through a rolling horizon DRP process. These uncertainties, such as demand fluctuations, machine failures, and delivery delays, cause instability and worsen the bullwhip effect, which reduces the reliability and effectiveness of production plans. To address these challenges, we propose a data-driven robust optimization framework that uses historical data analysis and clustering techniques to create well-defined uncertainty sets. Numerical experiments, using simulated historical data, show that this approach improves supply chain planning by balancing precision and robustness in managing uncertainties.

Keywords: Inventory control, production planning and scheduling, Decision Support System, Optimisation Methods and Simulation Tools
Abstract: The lot sizing problem involves meeting the demand of the clients of a given retailer by placing orders with different suppliers. Decision-making models were developed to minimize the retailer's total cost while considering their retailers' different characteristics, such as their capacities, prices, and lead times. However, when integrating uncertainty into the environment, this challenging problem becomes even more complex, and the exact methods developed need extremely long computational time to go over all the possible scenarios. This paper proposed a novel formulation to minimize the operations needed when finding the optimal solution to the lot sizing problem under uncertain lead times.

Keywords: Operations Research, Industrial and applied mathematics for production, Robustness analysis
Abstract: We consider the problem of simultaneously (a) finding a limited assortment of stock items and (b) cutting them with minimum trim loss to fulfill a known demand of different parts. We develop a framework to address the problem when stock sheets can be affected by faults, providing and testing a new bilevel model for both defect occurrence and robust optimization of the production process. The model generalizes an existing bilevel approach and overcomes some of its drawbacks, returning more credible scenarios in terms of defect distributions. Copyright© 2025 IFAC.

Keywords: Supply chains and networks, Supply Chain Management, Modelling Supply Chain Dynamics

Keywords: Supply chains and networks, Operations Research, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Traditional approach to Supply Chain Network Design was mostly intended to find a good trade-off between total income and various sustainability costs. However, recent extreme global events such as natural disasters, pandemics, climate change, and geopolitical conflicts, make critically important the incorporation of resiliency criteria to assess the survivability of the modeled system. We introduce the multi-objective Resilient Supply Chain Network Design Problem (RSCND) aimed to design Pareto-optimal families of production plans with respect to the following key objectives: min-max total cost and probability of a failure imposed by aforementioned disruptions. To obtain such designs, which balance aggregated sustainability (economical, social, environmental etc.) costs with survivability of the system, we propose a general scheme of a branch-price-and-cut algorithm.

Keywords: Complex adaptive systems and emergent synthesis in manufacturing, Design and reconfiguration of manufacturing systems, Enterprise modelling, integration and networking
Abstract: In today's rapidly evolving market, which increasingly favours sustainable products, manufacturing companies must turn to the development of modular product platforms. However, the complexity arising from developing complex products and the increasing demand for mass personalization brings forth multiple challenges. This preliminary study identifies and ranks the challenges encountered in designing modular product platforms to meet a demand for high-variety sustainable products. A twopronged methodology combines semi-structured and structured interviews with ten experts from three companies. This approach reveals seven categories of challenges. The results show that specific challenges become more critical depending on the company's context and maturity. Thus, addressing the seven groups of challenges requires consideration of the particular situation of each company. Identifying these groups of challenges will allow for developing diagnostic frameworks and product development strategies to address them in future research. Furthermore, integrating interventions more strongly focused on sustainable product design remains to be fully developed within the processes and tools present in current product development strategies.

Keywords: Smart manufacturing systems, Knowledge management in production, Robotics in manufacturing
Abstract: During the early stages of the manufacturing process, Computer-Aided Design (CAD) to Computer-Aided Manufacturing (CAM) transition is very common. Design-to-order companies cannot afford to operate manually the design and planning computer-aided software chain for each new custom product. The core of this research is: why wasting efforts to rediscover “how” to manufacture “what” is often already known at the design stage. The proposed approach removes the dependency to the CAD-to-CAM link and to its mandatory human support. Rather than identifying the manufacturing instructions from a drawing, it is simpler to generate instructions directly from an input data model and obtain a drawing by simulation of these instructions.

Keywords: Industry 4.0, Human-Automation Integration, Decision-support for human operators
Abstract: In the realm of manufacturing, efficient cost estimation from customer data like CAD drawings is essential for optimizing resource allocation and maintaining a competitive advantage. In a use case of a Small-Medium-Equipment Manufacturer (SME), specializing in the production of machine and plant parts, this meant significant challenges with their manual, time-intensive process for extracting dimensions from Computer-aided-Design (CAD) drawings, which involved entering data into spreadsheets for material cost estimation. This process required substantial cognitive effort and was prone to human error. To solve this issue, this paper proposes a user-centric software design leveraging Optical Character Recognition (OCR) to detect and display dimensions from CAD drawing. These efforts are are aligned with Industry 5.0 principles to enable a hybrid-intelligence based approach for extracting the right data. This selective interaction enhances human-machine collaboration and enables operators of various skill levels to participate in the process. The solution aims to streamlines data extraction, minimize errors, and improve operator efficiency while maintaining operator-control.

Keywords: Decision Support System, Transportation Systems, Smart transportation

Keywords: Sustainable Manufacturing, Design and reconfiguration of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: This paper presents a framework for computational intelligence in sustainable manufacturing modelling, focusing on the development of a green product configurator. Leveraging the Product Variant Master (PVM) structure and aligned with EN15804 standards, the framework integrates modular lifecycle stages: raw materials, manufacturing, transportation, use, and end-of-life into a causality-driven rule engine for dynamic Environmental Product Declaration (EPD) calculations. By defining detailed back-end attributes and rulesets, the configurator enables high-level user decisions, such as selecting "sustainable materials" or "low-emission manufacturing," while transparently computing their lifecycle impacts in accordance with ISO 14025. This approach bridges the gap between user-friendly interfaces and the complexity of lifecycle sustainability assessments, providing manufacturers with a decision-support tool for balancing design flexibility with environmental accountability. Future research directions include enhancing real-time feedback systems and exploring interactive user interfaces to further align sustainability objectives with decision-making in product configuration.

Keywords: Sustainable Manufacturing, Human-Automation Integration, Industry 4.0
Abstract: This paper introduces a pilot study investigating the impact of a back-support occupational exoskeleton during Manual Material Handling. The study focuses on examining parameters such as the pressure distribution of the feet on the ground, in particular the distribution of pressure between the forefoot and the rearfoot, two physiological parameters such as pulse rate and blood oxygen saturation, and general perceptions of comfort and usability. Findings reveal differences in the distribution of pressure on feet to the ground between the test performed with and without the exoskeleton. This research provides a foundation for future studies to further explore these findings and contribute to the enhancing the knowledge base in the field of workplace human factors.

Keywords: Human-Automation Integration, Smart manufacturing systems, Industry 4.0
Abstract: Human Digital Twins (HDT) are a key technology to enable human-centric manufacturing systems. This paper presents a novel meta-model for HDTs, grounded in general systems theory, to provide a unified and robust conceptualisation for HDT development. The model describes four essential interacting systems categorised by the physical or digital nature of their components; (i) the Human Individual is a physical system composed of a physiological, cognitive and mechanical sub-system among others; (ii) the Human Digital Twin is a digital system composed of a human model, data storage and management capabilities, and functions to generate feedback for the human; (iii) Sensors are cyber-physical systems capturing observations about the human and digitising them for the HDT; (iv) Human-Machine Interaction Devices are cyber-physical systems enabling the human to receive feedback form the HDT and interact with it. The systemic grounding provides a general model of an HDT as an extension of a DT representing any kind of system. It clarifies the relations and interfaces between its four main sub-systems, as well as the data flows and feedback mechanism. The HDT's Communication Interface is identified as key component to integrate HDTs into larger digital ecosystems and enable human-centric system development.

Keywords: Human-Automation Integration, Decision-support for human operators, Industry 4.0
Abstract: The Industry 5.0 concept emphasizes human-centric manufacturing and logistics systems, encouraging decision-makers to consider human well-being in management operations alongside efficiency and cost. This study addresses the challenge of physiological load and health implications for order picking workers in warehousing systems, aiming at comprehensively assessing the ergonomic workload level of workers. We conduct a laboratory experiment and collect both subjective fatigue perception and objective physiological signals, especially surface electromyography (sEMG) signals of order pickers using Borg's CR-10 scale and wearable physiological devices. Experimental data was processed into relevant features of basic physiology, task, and sEMG signals for order picking operations. We then developed six machine learning methods for model training and evaluation to achieve an accurate portrayal of ergonomic workload, and CatBoost achieved the best training performance. Further ablation analysis revealed that incorporating sEMG features allows for a more accurate description of ergonomic workload. This research provides a scientific approach for assessing ergonomic workload, which can benefit academics, managers, and decision-makers in developing human-centric order picking systems that balance efficiency with worker well-being, in line with the principles of Industry 5.0.

Keywords: Decision-support for human operators, Decision Support System, Production planning and scheduling
Abstract: The production supervisor job is known to be highly stressful, requiring humans to react quickly to complex and unforeseen socio-organizational situations, often under high time and hierarchical pressure, where inappropriate reactions can have disastrous economic consequences. In such situations, decision support systems (DSS) can be providential to help decision-makers make the best of their decisions, provided that such systems meet the desired levels of usability, acceptability, and effectiveness. Focusing on a particular industrial use case, this paper presents the outcomes of a design process centered on human needs, that keeps supervisors at the heart of the decision-making. We detail the chosen DSS architecture, the essence of the decision problems faced by supervisors, as well as a constraint programming (CP) approach able to deal with them. The paper also shows how CP can be coupled with human-computer interactions, and proposes an ecological serious game that enables to experimentally assess the usability, acceptability, and effectiveness of the DSS.

Keywords: Human-Automation Integration, Decision-support for human operators, Smart manufacturing systems
Abstract: Effective cooperation with complex cyber–physical systems is essential for advancing human-centered industrial environments. This paper introduces a Cooperation Grid Framework designed to enhance dynamic task allocation between humans and CPS agents. An application in an emulated environment demonstrates the framework’s ability to identify gaps and propose targeted improvements, thereby enabling efficient, robust, and adaptive human–agent cooperation.

The study emphasizes cooperation as a concept distinct from interaction, collaboration, and symbiosis, ensuring that human–CPS relationships are optimized. By providing practical insights for designing adaptive systems that support dynamic task allocation across multiple levels, the framework establishes a solid foundation for resilient, human-centered industrial systems capable of responding to evolving operational demands.

Keywords: Risk Management, Production planning and scheduling, Line Design and Balancing
Abstract: Scheduling processes in the aircraft final assembly line (FAL) presents a complex combinatorial problem involving hundreds of interdependent jobs, each constrained by precedence and limited resources. The difficulty is amplified by uncertainties in job durations, influenced by variable working conditions, unexpected disruptions, and unforeseen events. A stable schedule is essential, as changes can disrupt downstream processes like material supply and transportation, resulting in significant costs. In our work, we introduce two stability measures to quantify the impact of schedule disruptions: the expected sum of start-time deviations, referred to as "overlaps," and the maximum expected overlap. Our goal is to minimize the makespan while enhancing schedule stability through the development of several constraint programming (CP) models. The problem is modeled as a Stochastic Resource-Constrained Project Scheduling Problem (SRCPSP). In the deterministic version, with fixed job durations, the scheduling task involves assigning each job to a worker and selecting a start time that satisfies precedence and resource constraints, making it an NP-hard problem. In the stochastic version, job durations are random variables with known probability distributions and expected values. We use a proactive-reactive scheduling strategy. The proactive phase creates an initial schedule based on expected job durations, aiming to minimize makespan. In the reactive phase, disruptions are handled using a right-shift policy to maintain feasibility. Stability is measured by the difference between a job’s initial and final start times, termed "overlap." The two proposed stability metrics are incorporated into the objective function. We developed three CP models using CP Optimizer to address the stochastic SRCPSP. To evaluate their performance, we conducted an experimental study with 100 test problems, each containing 50 jobs. Job durations were sampled from three types of distributions: discrete uniform, and discrete approximations of normal and exponential distributions. Results show that model performance varies across different scenarios, highlighting the need for scenario-specific model selection in industrial applications.

Keywords: Industry 4.0, Knowledge management in production, Decision-support for human operators
Abstract: Understanding the workforce’s ability to achieve organizational goals is crucial for any manufacturing company. The success and stability of a company depend on the individual worker’s proficiency in achieving their work goals. A comprehensive competence assessment becomes essential to gain insights into an operator’s ability to perform manufacturing tasks effectively. Such competence assessment can track and maintain “up-to-date” information about operators’ competence level(s) and provide a detailed understanding of their performance. Via this knowledge, manufacturing companies can strategically consider assigning operators to specific workstations or adapting training processes to strive for organizational excellence. This research compares the accuracy of self-assessment and supervisor assessment with production KPIs for manual assembly tasks. Results indicate discrepancies between the operators’ self-assessments and the production KPIs, underscoring the lack of precision of this competence assessment method. Conversely, a significant correlation was found between the supervisor’s assessment and the same production KPIs. This study provides valuable insights into competence level assessment for manual assembly tasks.

Keywords: Line Design and Balancing, Heuristic and Metaheuristics, Robotics in manufacturing
Abstract: The rapid advancements in robotics and automation have revolutionized the manufacturing industry, introducing new complexities in the design and balancing of robotic assembly lines. The Robotic Assembly Line Balancing Problem (RALBP) extends the classical assembly line balancing problem by incorporating equipment selection for each workstation. In other words, RALBP requires tasks to be grouped based on shared equipment needs. As RALBP is an NP-hard problem, efficient metaheuristics must be developed to design realistic assembly lines with the fact that such lines can span several kilometers. This paper tackles these challenges by introducing a novel Restrict-and-Fix (RF) method, inspired by the Relax-and-Fix framework. The proposed heuristic decomposes the RALBP into smaller, manageable subproblems, solving them iteratively to find good quality solutions within reasonable computational times. Numerical results demonstrate the method's strong performance and scalability. Furthermore, as the method relies on mixed-integer linear models, it can easily be extended to account for a wide range of practical constraints.

Keywords: Line Design and Balancing, Design and reconfiguration of manufacturing systems, Optimisation Methods and Simulation Tools
Abstract: The increasing complexity of market demands requires modern production systems to achieve high flexibility while efficiently handling diverse product variations. Traditional single-model mass production systems are inadequate, driving the adoption of mixed-model strategies that enable multiple products to be assembled on a single line. This shift emphasizes the need for advanced assembly system configurations that balance adaptability, efficiency, and workforce variability. This study investigates the Mixed-model Fishbone Assembly Line Worker Assignment and Balancing Problem (MFALWABP). The Fishbone Assembly Line (FAL) features a central backbone with two parallel workstations per stage, allowing products to flow freely and independently. This configuration supports asynchronous operations, enhances flexibility, and enables efficient handling of worker efficiency differences. A hybrid approach combining optimization and simulation is proposed to address the challenges of task balancing and worker allocation in FALs. A Mixed-Integer Linear Programming (MILP) model, developed in CPLEX, minimizes costs and workload imbalances while considering worker efficiency and stage activation. This initial optimization is complemented by a MATLAB-based simulation model that evaluates system dynamics, including buffer utilization, process times, and cycle time variations. The iterative framework refines solutions through two strategies: Single-Sequence Refinement, which optimizes a given product sequence, and Multi-Sequence Refinement, which evaluates multiple sequences to ensure robust performance under varying conditions. The proposed methodology provides a practical tool for optimizing FAL design and management, offering valuable insights for improving productivity and adaptability. This research contributes to the development of efficient, flexible assembly systems, addressing both worker heterogeneity and product variability in modern production environments.

Keywords: Line Design and Balancing, Supply chains and networks, Inventory control, production planning and scheduling
Abstract: This study presents a comprehensive mixed-integer linear programming model that integrates supply chain network design with assembly line balancing under uncertainty. The model simultaneously addresses strategic and tactical decision-making, optimizing supply chain performance while managing costs and risks. It incorporates Conditional Value at Risk to account for demand uncertainties, ensuring resilience against worst-case scenarios. The supply chain configuration includes suppliers, manufacturers, assemblers, and collection centers, with tasks assigned to straight assembly lines, respecting precedence relationships and capacity constraints. Computational results demonstrate the model’s effectiveness in minimizing total costs and achieving robust performance across multiple demand scenarios. Sensitivity analyses highlight the trade-offs between cost efficiency and risk aversion, providing decision-makers with valuable insights into balancing resilience and operational efficiency.

Keywords: Supply chains and networks, Supply Chain Management, Optimisation Methods and Simulation Tools
Abstract: The automotive industry is currently facing significant uncertainties and challenges. At the same time, efforts to achieve emission-free mobility are leading to power train diversity. In this complex environment, it is essential for car producers to define an efficient and resilient production strategy of future car production networks. This article provides a universal approach and simulation model to evaluate production strategies considering power train diversity. A case study, mirroring possible scenarios for automotive manufacturers, shows that a certain proportion of mix production can have an advantage in terms of resilience compared to a highly efficient pure-variant network, especially by marked uncertainties.

Keywords: Supply Chain Management, Optimization and Control, Supply chains and networks
Abstract: The last twenty years has seen significant advances in the area of supply chain modelling and the development of strategies for the control and optimization of supply chain operations. Developments in the modelling of distributed systems in which aspects of autonomous operation and decision making can be incorporated has been very significant in the ability to represent supply chains in a more realistic manner. Further, increased computing capabilities coupled with significant advances in the availability of the off-the-shelf optimisation tools means that large-scale optimisation problems such as those relevant to supply chain behaviour can be addressed. These advances have stimulated a huge amount of activity in the academic literature yet to date the ability to deploy related tools industrially has been limited. Some of the reasons for the limited uptake can be traced to misconceptions about the nature of information and knowledge that is practically available relating to supply chain behaviour and the constraints on being able to influence supply chain behaviour even when the right information is available.

This extended abstract seeks to raise and highlights some of the practical challenges associated with information and control of supply chain operations to show why modelling and optimisation might not be as simple to use in practice even with today’s technological advances. In fact the ability to simply just map a supply chain or network completely - that is identify all the major players and their connections - is often extremely challenging. Drawing on the author's experience working directly with companies and their supply chain connections, the extended abstract focusses on three areas of challenges: • Structural Uncertainty: Issues arising because the structure of the Supply Network is uncertain • Information (Un)availability: Challenges associated with the information needed for modelling and/or optimisation not being available • Decision & Control Limitations: Limitations arising because it not possible to deploy control/coordination of all aspects of the supply chain

Keywords: Supply chains and networks, Sustainable Manufacturing, Design and reconfiguration of manufacturing systems
Abstract: In the current dynamic, volatile, and uncertain world, manufacturing companies face increasing pressure to respond efficiently and rapidly to disruptions, while at the same time adopting sustainable and circular practices. This paper focuses on Manufacturing-as-a-Service (MaaS) for enhancing resilience and promoting circularity in manufacturing value chains. Based on the limitations of existing MaaS re-search and scarcity of implementations, a systematic methodology is proposed for manufacturing process servitization, manufacturing process and product matching, and manufacturing process connection, collaboration and execution. This methodology supports the scaling of MaaS to a large variety of manufacturing processes, enabling dynamic distributed networks of manufacturing resources. Resiliency enhancing processes are outlined using MaaS for creating alternative value chain orchestrations, as responses to disruptions. Moreover, processes promoting circularity in relation to MaaS and resilience are outlined. This act as support for finding appropriate value chains partners to enable a circular prod-uct system and in increasing resilience and circularity simultaneously by reusing and remanufacturing critical components.

Keywords: Risk Management, Smart manufacturing systems, Supply Chain Management
Abstract: Supply chain resilience (SCR) has been a key focus for researchers and businesses over the past decade. The Covid-19 intensified this attention, highlighting critical issues such as raw material shortages, unpredictable demand shifts, and logistics and supply chain challenges. These disruptions have significantly impacted global supply chains in the last two years. Manufacturing-as-a-Service (MaaS) has emerged as a new service-oriented manufacturing paradigm inspired by cloud manufacturing. MaaS has the potential to support companies in increasing their SCR, e.g., by providing access to a wide pool of suppliers. However, literature is scarce in investigating this relationship. This paper aims to fill this gap through 15 expert interviews exploring the link between SCR capabilities and MaaS. Results suggest that MaaS supports supply chain flexibility, velocity, redundancy and visibility. For the experts, MaaS has a detrimental effect on supply chain collaboration, which is considered an antecedent of SCR for literature. Finally, the functionalities needed in a MaaS system to support SCR are discussed. Copyright © IFAC 2025

Keywords: Supply Chain Management, Sustainable Manufacturing
Abstract: The mining industry is crucial for agriculture and consumer goods but faces challenges like fluctuating demand, price volatility, and environmental concerns. Increasing resource consumption, particularly water and energy, requires careful management amid water scarcity and climate change. Sustainability, agility, and resilience (SAR) in the supply chain are vital for optimizing operations, reducing environmental impact, and mitigating risks. However, a lack in the literature of standardized, practical measures to traduce and enhance SAR hinders their advancement. This paper addresses this gap by organizing SAR strategies and KPIs from existing research, offering actionable measures. It provides an overview of SAR in mining and examines a case study of a Moroccan mining company, evaluating the integration of these concepts in mine exploitation and rehabilitation.

Keywords: Design and reconfiguration of manufacturing systems, Decision Support System, Operations Research
Abstract: The critical decisions related to production system configuration are often supported by mathematical optimization tools, such as mixed-integer linear programming (MILP) models built into automated system design software. However, users may have concerns about the computed optimal solution--or about the absence of a solution--arising from discrepancies between the mathematical model and their understanding of the problem, or from the flexibility of input parameters. Thus, to support the decision-making process, it is crucial to make optimization explainable, i.e., to reinforce the understanding of the user why a given solution is recommended. This paper proposes an interactive procedure to solve this problem, where the decision-maker asks consecutive “why not c” type questions, where c is a feature of the solution encoded in a set of constraints on the decision variables of the MILP. Special focus is given to the case where infeasibility must be explained. Two alternative search procedures were implemented to find all possible explanations of infeasibility. The proposed approach was validated on a medium-scale sample instance of the system configuration problem with 17 tasks requiring 13 resources for their execution. The gained explanations can help identify the parameters and constraints that require special attention by the user.

Keywords: Sustainable Manufacturing, Enterprise modelling, integration and networking, Modelling Supply Chain Dynamics
Abstract: Digital Product Passports (DPPs) can play an essential role in accelerating the circular economy. However, the process of DPP implementation and adoption in the Electrical and Electronic Equipment (EEE) sector remains slow and unclear, highlighting a critical gap in the existing research. This study aims to understand how transition dynamics impact DPP adoption in the EEE sector. Based on a multi-level perspective on socio-technical transitions, DPPs are positioned as a technological niche innovation that influences actors, transition dynamics, and the corresponding adoption of DPPs. Through a literature review, expert interviews, and case studies, two socio-technical transition pathways are proposed to explain the evolution of the EEE sector and their implications for DPP adoption. The designed pathways illustrate how multiple factors at the landscape, socio-technical regime, and niche innovation levels interact and shape the EEE sector’s response to macro pressures and the mandatory implementation of DPPs by the European Union. A critical insight here is that DPPs should not be viewed solely as a technological concept. Rather, a broad set of interconnected factors plays a decisive role in the successful adoption of DPPs by the EEE sector.

Keywords: Discrete event systems in manufacturing, Line Design and Balancing, Production planning and scheduling
Abstract: Simulation is an essential tool for analyzing manufacturing systems in the Digital Transformation context. However, there is a lack of proper methods for leveraging established improvement tools with simulation models so that a combined approach can better support decision-making. This paper describes and applies a procedure for combining data-driven simulation models with production balancing techniques in manufacturing systems. The results obtained in a real-world application demonstrate the capability of the proposed simulation-based production balancing combined approach for effectively allocating limited available resources improving manufacturing operational efficiency.

Keywords: Decision Support System, Optimization and Control, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The world is facing a series of industrial challenges. Competitive pressure, a shortage of skilled workers and financial limits, especially for small and medium sized enterprises, simultaneously cause the desire to increase efficiency and the appearance of challenges. The key drivers of solutions are digital transformations and optimization processes. This paper focuses on the combination of a PPR-based data model, which is equivalent to a comprehensive product twin, with performance indicators. This symbiosis enables the use of data from the complete life cycle of a product, it´s creation processes and the resources used within to generate useful information. This information forms the basis for company-relevant decisions. Both the basis for the product twin model as well as the performance indicator integration process is developed in order to achieve such information. Using the developed processes, any performance indicators with associated data can be integrated into the model to form the basis for decisions about optimization or transformation activities.

Keywords: Decision Support System, Production planning and scheduling
Abstract: Assembly processes account for over 50 % of production time and are still largely manual in highly variable industries. Current planning is based on expert knowledge and previous planning results, leading to inefficiencies with new workers. This study presents a deep learning model that uses historical process data to support similarity- and variant-based planning. The model predicts subsequent process steps and components based on a given process sequence by training a Bidirectional encoder representations from transformers (BERT)-based architecture on 280 different work instructions, achieving a combined accuracy of 94 % and an F1-score of 82 %. The results show the potential of assisted planning in creating work instructions. However, it is impossible to generate work instructions for the planning of completely new products.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Transportation Systems, Operations Research
Abstract: This paper presents a mixed integer linear programming model for optimizing maintenance and renewal costs for urban rail networks over mid- and long-term horizons. By focusing on planning maintenance and renewal activities, the proposed model optimizes expensive renewal and maintenance costs over the lifetime of the rail curve while considering industrial constraints related to the degradation of rail tracks and rail possessions. The proposed optimization model is compared to a baseline rule-based strategy, demonstrating significant cost reductions, with savings of up to 6.93% in some cases and a decrease in the number of performed maintenance activities. The optimization approach adjusts renewal dates to balance curve lifespan extension and cost minimization, leading to earlier renewal dates. This reduction in activities also contributes to sustainability by reducing environmental impacts. The long-term implications of the model are discussed. In many cases, considerable cumulative cost savings and indirect environmental benefits are achieved by the proposed approach.

Keywords: Design and reconfiguration of manufacturing systems, Monitoring, diagnosis and maintenance of manufacturing systems, Operations Research
Abstract: Green hydrogen (GH), produced via electrolysis powered by renewable energy sources (RESs), offers a sustainable energy storage solution. Designing GH production networks is challenging due to the variability introduced by the intermittency of RESs and the reliability of interdependent system components. This study uses optimization to aid GH system design, specifically subsystem sizing and technology selection, by minimizing a newly formulated levelized cost of hydrogen (LCOH). This formulation incorporates a reliability metric, lost GH due to downtime. The model achieves an LCOH of 3.19 USD/kg for the analyzed system. The findings reveal how variations in uptime ratio (UTR) and demand affect LCOH and power curtailment. Threshold regions highlight the trade-offs between investing in system maintenance to improve UTR for a lower LCOH and leveraging enhanced UTR to reduce power curtailment by an efficient design.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Monitoring, diagnosis and maintenance of manufacturing systems, Smart manufacturing systems
Abstract: Industrial maintenance is crucial for operational efficiency, requiring precise and timely decision-making to minimise downtime and costs while preserving system reliability. This paper presents a framework that integrates Deep Reinforcement Learning (DRL) with Explainable AI (XAI) to optimise preventive maintenance strategies and enhance interpretability for human operators. By applying XAI methods to DRL-driven decisions, the framework offers transparent insights into the rationale behind recommended actions, bridging the gap between AI-generated output and operator trust. Our work contributes to the post-hoc interpretability branch of Explainable Reinforcement Learning (XRL) and aligns with the objective of making DRL models both powerful and comprehensible in high-stakes industrial contexts. The simulation results demonstrate notable reductions in downtime, along with improved system reliability and operational transparency. These findings underscore the potential of merging DRL and XAI in industrial maintenance, creating opportunities for more effective and trustworthy AI solutions in real-world settings.

Keywords: Sustainable Manufacturing, Operations Research, Optimization and Control
Abstract: Increasing value and complexity in warranty contract design has made policy optimization necessary for manufacturers. This paper investigates a one-dimensional, non-renewing hybrid warranty with ``as-good-as-new" (AGAN) replacements. The warranty spans two periods. AGAN remanufactured second-hand parts are used as replacements in the first period, while minimal repairs are provided in the second period. However, customers may pay a premium to instead extend the AGAN replacements into the second period. A nonlinear optimization model is proposed for maximizing manufacturer profit based on the ratio between first and second warranty period length, the ratio between base unit price and enhanced warranty cost, and the age at which second-hand parts are salvaged rather than remanufactured. The proposed warranty is a novel extension in sustainable manufacturing and has its value demonstrated via numerical experiments while linking basic product aspects to warranty design.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Decision Support System, Industry 4.0
Abstract: In modern process industries, ensuring reliable fault diagnosis is essential for maintaining product quality, operational safety, and cost efficiency. Traditional data-driven classification methods perform well under stable conditions but do not provide any mechanisms to quantify uncertainty when the model lacks confidence in its predictions, which is critical in real-world industrial environments. Conformal prediction addresses this limitation by providing rigorous uncertainty sets of these classification techniques. This paper compares classical classification methods with their conformal counterparts using two widely recognized benchmark datasets: the Tennessee Eastman Process (TEP) and a Continuous Stirred Tank Reactor (CSTR). We focus on identifying fault types and evaluate each approach in terms of accuracy, misclassification rate and coverage. Our findings demonstrate that conformal prediction improves the confidence and robustness of fault diagnosis, providing classifier with a more reliable and uncertainty-aware diagnostic framework for dynamic industrial environments.

Keywords: Discrete event systems in manufacturing, Simulation technologies, Optimisation Methods and Simulation Tools
Abstract: Abstract: Creating simulation models and running experiments requires expertise and significant effort. Modelling complex production systems by using Petri Nets, which own a simple formalism, leads to considerable modelling effort. To reduce the workload required, fundamental processes within a production system must be analyzed and represented as generic Petri Net structures to enable their automatic generation. These structures can serve as modular building blocks to construct arbitrarily complex systems. The broader objective is to enable the automatic generation of such, based on process descriptions within a simulation framework. Built on these findings, the necessary information for developing a specialized formal process description is derived, enabling the automated generation of Petri Net simulation models for production systems.

Keywords: Supply chains and networks, Supply Chain Management, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: This article presents a data management plan for a digital platform with open and restricted data that connects companies in a region and enables them to exchange data. The literature research shows that there is no standardized concept in the field of data management. Furthermore, the combination of open and restricted data on one platform has not yet been investigated in the context of data management. The data management plan is developed on the basis of an application example of a digital platform for a district. The data management plan developed can serve as a template for similar projects in other regions and can be seen as a basis for the development of innovative data management solutions. The developed platform thus offers numerous starting points for economic optimization, political support measures and the development of long-term data strategies. It also opens up new research approaches, for example for the development of innovative analysis methods or data-driven business models.

Keywords: Discrete event systems in manufacturing, Modeling, simulation, control and monitoring of manufacturing processes, Simulation technologies
Abstract: Creating digital twins (DT) of cyber-physical systems (CPS) presents unique challenges that current modelling and simulation (M&S) tools are not fully equipped to address. While some aspects of this challenge have been well explored, such as multi-domain modelling, real-time data synchronisation, and predictive analytics using AI and machine learning, others have received less attention. Two important, yet in our opinion underexplored, areas are (i) the handling of large systems of differential and algebraic equations (DAE) coupled with event-based dynamics, as Finite State Machine (FSM), and (ii) the need for not only efficient simulation code but also for efficiency in the process of generating that code. We discuss the challenges related to these topics in a CPS-based context, and sketch out a roadmap for developing solutions that enhance the effectiveness and scalability of M&S tools.

Keywords: Production planning and scheduling, Heuristic and Metaheuristics, Operations Research
Abstract: The integration of machine learning (ML), particularly reinforcement learning (RL), techniques with classical optimization methods has shown significant potential in addressing the computational challenges of complex combinatorial problems. This paper presents a novel approach that enhances the Branch and Bound (B&B) algorithm with Q-learning, a type of model-free RL, applied to the Job Shop Scheduling Problem (JSSP). While B&B is a powerful method for finding optimal solutions, its computational demands are often prohibitive for large instances. By incorporating Q-learning into the B&B framework (BBQL), the proposed method learns to prioritize promising branches, thereby improving search efficiency and reducing computational overhead. We evaluate BBQL against the classic B&B algorithm on standard JSSP benchmarks. Experimental results indicate that BBQL achieves better makespan results with significantly lower computational costs, highlighting the potential of reinforcement learning to enhance traditional optimization frameworks in scheduling applications. This research offers insights into how AI-driven guidance can improve exact optimization methods, providing a scalable solution to complex scheduling challenges.

Keywords: Supply chains and networks, Heuristic and Metaheuristics, Supply Chain Management
Abstract: This study focuses on a circular tire supply chain network design problem in the context of extended producer responsibility. To maximize the expected benefit of the producer under uncertainty, this study provides a scenario-based multi-period mixed integer nonlinear programming model formulation. In the network, a producer can either establish the recycling centers by themselves or collaborate with third-party recyclers. The model helps us decide the optimal recycling option to reduce the effect of the extended responsibilities under budget and capital constraints. A metaheuristics algorithm, namely simulated annealing-enhanced genetic algorithm with gradient-based systematic repair is proposed as a solution approach. Based on two illustrative examples, we found that implementing extended producer responsibility in the linear tire supply chains results in a significant reduction in profits of the producer. However, collaborating with the cost-effective recyclers, rather than establishing a new recycling center, may limit this reduction in profits, especially for the producers with capital constraints. A key contribution of this study is that it provides decision-making insights regarding extended producer responsibility implementation in the tire industry.

Keywords: Sustainable Manufacturing
Abstract: Constantly rising carbon emissions are closely linked to the intensified evidence of global warming, underscoring the urgent need for sustainable strategies and technologies to reduce carbon emissions. The manufacturing sector, particularly steelmaking, is a significant contributor to emissions due to its heavy reliance on fossil fuels. This paper expands on previous research by exploring the integration of renewable energy in energy-intensive industries. It evaluates CO2 emissions reduction, but also assesses the financial viability of the use of hybrid renewable energy systems with storage technologies to eliminate fossil fuel dependence, using a secondary steelmaking industry as a case study.

Keywords: Sustainable Manufacturing, Design and reconfiguration of manufacturing systems
Abstract: Hydrogen combustion is a promising technology that has emerged as an alternative to natural gas use in glass production, offering a pathway to decarbonize the industry. However, the widespread adoption of hydrogen faces challenges, including high demand and long production times for electrolyzers, which hinder rapid scale-up. As a practical alternative, hydrogen can be supplied via tube trailers, though large-scale testing and empirical insights remain limited in both practice and research. This study applied a multiple-case study approach to investigate the feasibility of scaling hydrogen use in glass production through tube trailers, focusing on two distinct glass industries. The findings demonstrate that hydrogen is a viable substitute for natural gas in short-term, large-scale tests, requiring only minor supply modifications. Moreover, the transition does not necessitate significant changes to furnace operations, nor does it adversely affect glass quality or emissions of harmful gases. These insights contribute to advancing the practical application of hydrogen in the glass industry and highlight key considerations for its integration.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Decision Support System, Sustainable Manufacturing
Abstract: The glass manufacturing industry is a key contributor to various sectors, including construction, automotive, and packaging. However, it is also energy-intensive and contributes significantly to global carbon emissions. Decarbonizing glass production is essential for aligning industrial practices with global climate goals. This study focuses on advancing sustainability in glass manufacturing through a predictive maintenance planning framework adapted to critical forming components, including Gob Delivery System, Blank Moulds, and Blow Moulds. By optimizing maintenance schedules and minimizing unplanned downtimes, the framework reduces resource wastage, energy inefficiencies, and associated carbon emissions, thereby aligning operational practices with sustainability objectives. The proposed framework integrates reliability analysis, cost evaluation, and advanced optimization techniques to dynamically generate maintenance schedules. A computational tool developed for this purpose simulates degradation and maintenance processes, offering actionable insights into component reliability and cost efficiency. While validated using simulated data, the methodology is adaptable for broader industrial applications, promising significant contributions to the sustainability of glass manufacturing.

Keywords: Facility planning and materials handling, Smart transportation, Supply chains and networks
Abstract: The rapid growth of e-commerce and rising customer demand for fast, efficient order delivery have accelerated the expansion of the supply chain and logistics sectors, making warehouses essential for business success. In response, robotic compact storage and retrieval systems (RCS/RSs) have emerged as an effective solution to enhance operational flexibility, space utilization, and energy efficiency in warehouses. In these systems, robots navigate the grid to retrieve/store bins. When a bin is requested, the robot removes any obstructing bins above it, temporarily placing them on neighboring stacks and returning them afterward. The movement of thousands of bins daily, frequent stops, and vertical movements facilitate energy regeneration. This study introduces an energy calculation framework for RCS/RSs and examines energy regeneration in a 100 (width) × 100 (depth) × 50 (height) grid, revealing energy gains of up to 12.1% in horizontal (x-y dimensional) movements and 26.4% in vertical (z-dimensional) movements.

Keywords: Decision Support System, Supply Chain Management, Fuzzy logic control
Abstract: Waste management through circular economy implementation is crucial for achieving sustainability and enhancing the performance of the waste-to-energy supply chain (WtESC). Therefore, developing key performance indicators (KPIs) and understanding their significance is essential for assessing WtESC performance. However, there is a lack of studies focused on developing and evaluating KPIs for WtESC. To address this gap, this study offers a novel machine learning (ML)-based decomposed fuzzy set (DFS)-analytical hierarchy process (AHP) model to assess the KPIs that can be used to evaluate the WtESC performance. Since decision-making based on experts' judgment often faces uncertainty and experts’ experience significantly impacts the final decision, the advanced ML-based DFS-AHP model can effectively handle these challenges and enhance the model’s reliability. In the proposed framework, decision makers’ weights are computed using the ML approach based on expert information, which is integrated into the DFS-AHP model. The results indicate that the most important KPI for WtESC is ‘CO2 emissions intensity’, which received a de-fuzzified composite weight of 0.1291. This KPI should be considered with a higher priority to ensure sustainability and improve WtESC performance. Consequently, the decision-makers should consider these findings when developing the performance index for WtESC, which may further assist in taking the necessary actions to improve WtESC's performance.

Keywords: Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes, Simulation technologies
Abstract: Virtual commissioning (VC) and digital twins (DT) are pivotal technologies in the Industry 4.0 (I4.0) landscape. However, their integration remains underexplored in the literature. This study proposes a structured methodology for optimising production line design and operation by integrating VC and DT. The proposed approach leverages two VC methodologies—Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL)—to facilitate algorithm testing in both simulated and real-world environments. Bidirectional, real-time data exchange between virtual and physical models is established using OPC UA and RTDE protocols, creating a digital shadow that continuously updates to reflect the active system. The proposed framework promotes lifecycle-wide use of simulation models, enabling seamless reuse from VC to DT, while demonstrating the interactive potential of these technologies. Furthermore, the study highlights the advantages for companies in adopting both VC and DT to enhance operational performance.

Keywords: Smart manufacturing systems, Production planning and scheduling, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Utilizing digital twins (DTs) in production planning and control (PPC) has emerged as a solution to enhance production system responsiveness to increasing demand variability. However, principles for designing and validating effective DTs are not explicitly clear. Furthermore, introducing DTs in PPC often requires redesigning the traditionally hierarchical PPC process. This paper answers the research question: ”What design process and design principles, models, and methods apply to the ODT design process and the associated PPC redesign process?” by proposing a systems engineering approach using an enriched Vee-model.

Keywords: Smart manufacturing systems, Knowledge management in production, Robotics in manufacturing
Abstract: Indoor localization in complex environments is essential for omni-channel applications in retail, healthcare, and logistics. This paper presents a solution combining semantic digital twins, robotic mapping, and mobile apps for accurate, user-friendly localization. Semantic digital twins, enriched with spatial context, dynamically represent physical spaces, while robots create detailed annotated maps. A QR code links mobile apps with these digital representations. The system aligns multi-application localization to a unified coordinate frame, validated in retail and residential settings. It addresses dynamic map updates and user-centric navigation, advancing semantic digital twins for broader applications.

Keywords: Human-Automation Integration, Decision-support for human operators, Robotics in manufacturing
Abstract: In current Cyber-Physical Systems, especially in Human-AI teams, the integration of Human Digital Twins raises several ethical concerns. To ensure fruitful completeness between human and technology, the design of the system must include representations of the human needs, abilities and acceptance. We propose a framework for a shared and common digital representation accessible to both human and artificial agents. This framework, based on the Human-Machine Cooperation principles, is illustrated by the example of a human-robot team in the context of a production system.

Keywords: Sustainable Manufacturing, Modeling, simulation, control and monitoring of manufacturing processes, Production Control, Control Systems
Abstract: This paper introduces an approach for generating Model-Based Sustainability Functions (MSF) in order to increase the scope of services and thus the potential value of technical systems. Based on company’s specific goals and circumstances, MSF seek to make economical use of data along the entire product lifecycle provided by Digital Twins. For the generation of these MSF, a three-stage approach is introduced. The validity of this approach is illustrated by the use case of calculating energy consumption during virtual commissioning of a delta robot.

Keywords: Sustainable Manufacturing, Industry 4.0, Supply chains and networks
Abstract: This study examines the application of Digital Product Passports (DPPs) in the smartphone industry, specifically focusing on the lifecycle of Fairphone’s battery. By analyzing the roles and information requirements of key stakeholders and conceptualizing the content of the DPP, it provides valuable insights into how DPPs can enhance information sharing and foster collaboration throughout the product lifecycle, ultimately supporting the transition to circular manufacturing.

Keywords: Sustainable Manufacturing, Production planning and scheduling
Abstract: Circular Economy (CE) has a low implementation rate so far, despite its many environmental and economic benefits. The concept of Upgrade Circular Economy (UCE) aims to address the existing challenges by aiming for a more continuous value enhancement of circular products and an industrialization of the associated processes. Digital Product Passports (DPP) are a key component of the concept as they act as a data hub for the circular value network. However, their prevalence in industrial practice is low. The aim of this work is to extend a concept for the integration of DPPs into the existing system landscape of manufacturing companies to advance the implementation rate. The core aspects of the work are the derivation of new use cases and data requirements that arise in an UCE, the formulation of evaluation options and implementation recommendations for the DPP, as well as examples for the concrete implementation of event technology. The aim is to provide manufacturing companies with practical options for the use of DPPs as a basis for the implementation of UCE.

Keywords: Sustainable Manufacturing, Decision Support System, Smart manufacturing systems
Abstract: A digital product passport (DPP) is a set of data specific to a product that includes the information of a product which can be electronically accessed through a data carrier. It can support sustainable production and assist consumers and businesses in making informed choices when purchasing products. There has been a growing interest in the applications of DPP in the research and business domains working on developing and implementing DPPs. Implementation of DPPs faces challenges such as high-quality data, data standards, knowledge barriers and IP rights. This study aims to provide insights from three cases implementing DPPs and compile the learnings and improvement possibilities across three industries: textiles, additive manufacturing, and manufacturing engineering. Blockchain technology to ensure data quality and smart contracts to control user access are common solutions for data security across all three use cases. Additionally, the use cases enhance the technology adoption of DPPs for all actors in the supply chain and integrate DPPs with life cycle assessment to evaluate energy consumption and carbon emissions, aligning with sustainability goals. The findings highlight the importance of a comprehensive strategy integrating technical innovation, standardisation, and education.

Keywords: Supply chains and networks, Smart manufacturing systems, Optimisation Methods and Simulation Tools
Abstract: The Digital Product Passport (DPP) is a transformative tool designed to enhance transparency and circular economy practices. However, the DPP holds potential to also improve production processes. This paper explores the dual potential of optimizing production processes using value stream analysis and supporting downstream lifecycle activities such as consumer decision-making, repair, and remanufacturing. Key data sources, interfaces, granularity and hierarchy levels within industrial systems are identified, and a conceptual framework for collecting, storing, and utilizing production data is proposed. Within a case study of remanufacturing several barriers are shown that prevent an automated data collection and utilization. This study concludes with recommendations for integrating DPPs into existing industrial infrastructures to maximize their potential for process optimization.

Keywords: Business Process Modeling, Supply chains and networks, Supply Chain Management
Abstract: To promote the environmental sustainability of products, manufacturers, as the resposible economic operators, must provide product-related information through a Digital Product Passport (DPP), which will soon become mandatory for a wide range of products, including furniture. As the field of DPPs evolves rapidly, manufacturers face several challenges related to their implementation. This paper examines the process of creating and issuing a DPP, focusing on the use of data spaces and ontologies as foundational elements. A concept for structuring relevant product information using a metadata schema is presented, enabling the construction of a DPP as a knowledge graph. Furthermore, the role of data spaces and decentralized identifiers in building a secure and interoperable digital infrastructure for exchanging product-related information is explored. An industrial case study at a furniture dealer demonstrates the practical steps for creating and issuing a DPP and offers insights into the necessary preparations for successful implementation.

Keywords: Decision Support System, Modelling Supply Chain Dynamics, Robustness analysis
Abstract: The sustainable aviation fuel (SAF) supply-chain faces challenges related to feedstock availability, environmental impact, and complex decision-making, requiring the identification of viable feedstocks, sustainable production pathways, and a structured framework to balance multidisciplinary criteria. This paper presents a prospective SAF feasibility study, based on UK feedstock availability, revealing that biodegradable municipal solid waste (BMSW) is the most viable. Cereal residues, although the second-most abundant, face predictability challenges, necessitating further study of their current uses and the development of new supply-chains. Fats, oils, and greases (FOGs), waste wood, and sewage sludge have minimal availability. A cradle-to-grave lifecycle assessment of BMSW, waste straw, waste wood, and FOGs use in SAF production confirmed at least a 90% global warming potential reduction compared to fossil jet fuel, aligning with existing literature. Three multicriteria decision analysis methods with four weighting schemes were applied to evaluate the SAF production pathways across 26 multidisciplinary criteria. MSW ranked highest, straw and waste wood achieved similar scores, and FOGs consistently ranked lowest. Together, these methodologies form an integrated framework for evaluating supply-chain resilience, sustainability, and decision-maker preferences to address multiple objectives.

Keywords: Supply Chain Management, Decision Support System, Modelling Supply Chain Dynamics
Abstract: Tea is a critical commodity that fuels the economies of numerous developing nations and is consumed daily by half the global population. It is the second most popular beverage in the world, following water. Tata Consumer Products, Unilever, and Twinings are among the major tea producers progressively dedicating themselves to sustainable sourcing. Integrating digital technologies and sustainability through Voluntary Sustainability Standards (VSSs) such as Rainforest Alliance, Organic, and Fairtrade can improve climate resilience and resolve the economic, social, and ecological challenges that tea smallholders encounter. This study presents a systematic methodology for evaluating and selecting VSSs through decision hierarchy and weighted criteria, providing practical tools for policymakers and industry stakeholders. The study indicates that market demand and price premium are crucial variables in the selection of VSSs. This underscores the necessity of matching sustainability certifications with consumer preferences and industry developments to improve acceptance and efficacy. Further, findings underscore the challenge of reconciling economic priorities, such as market demand and implementation simplicity, with social and environmental sustainability goals. This serves as a vital input for developing balanced VSS strategies.

Keywords: Decision Support System, Supply chains and networks, Inventory control, production planning and scheduling
Abstract: Additive Manufacturing (AM) is reshaping supply chain (SC) structures by enabling decentralised production, digital inventories, and on-demand manufacturing. These transformations demand new decision-making approaches to manage disruptions in SC configuration, inventory management, supplier selection, and manufacturing design. This study systematically reviews 27 peer-reviewed studies to assess decision-support tools—such as optimisation models, simulation techniques, and multi-criteria decision-making (MCDM) frameworks—used to facilitate AM integration. A structured mapping is proposed to map AM-induced SC changes to appropriate decision tools. The findings provide structured insights to enhance SC performance, adaptability, and resilience in AM-enabled environments.

Keywords: Supply chains and networks, Supply Chain Management, Operations Research
Abstract: Rising sustainability concerns underscore the importance of efficient closed-loop supply chains, especially for energy-intensive materials like glass. Despite being fully recyclable, a significant amount of glass waste ends up in landfills. Recent studies have highlighted the potential of recycled glass as a supplementary cementitious material in concrete production, replacing 10-30% of Portland cement. This substitution can lower CO2 emissions and mitigate waste challenges but involves trade-offs related to processing costs, environmental benefits, and material performance. This paper presents the first integrated reverse supply chain (RSC) model for Waste Glass (WG) recycling, designed to optimize profit while reducing carbon emissions. The proposed model aims to determine facility sizing, location costs, and reprocessing strategies within the recycling network. The model is validated through a case study with a regional waste recovery firm, offering actionable insights for advancing sustainable glass recycling practices.

Keywords: Supply Chain Management

Keywords: Modelling Supply Chain Dynamics, Supply chains and networks, Supply Chain Management
Abstract: This study introduces a novel decision support system (DSS) framework to model and assess three alternative material-driven logistics networks for secondary packaging systems (SPSs) in the fruit and vegetable supply chain. These SPSs cover reusable plastic containers (RPCs), single-use plastic containers (SPCs), and corrugated cardboard boxes (CCBs). By integrating primary data from enterprise resource planning systems with Geographic Information Systems, the DSS offers high-resolution insights into transportation flows, payloads, and environmental emissions. The framework captures key lifecycle stages of packaging and distribution systems, from the production and use to end-of-life management, enabling a robust comparative analysis of environmental performance. The proposed tool further assists the supply chain’s stakeholders in preparing environmental reports that account for the impacts generated by their annual logistics flows and enables the forecasting of future impacts by incorporating estimated flows. Applied to a large-scale logistics network of an Italian retailer, the DSS revealed significant trade-offs between reusable and single-use SPSs. The findings underscore the importance of data-driven tools for optimizing sustainability in food supply chain logistics and suggest avenues for future research to enhance the scalability and applicability of DSS frameworks in complex supply chains.

Keywords: Supply chains and networks, Modelling Supply Chain Dynamics, Optimization and Control
Abstract: Cold food supply chains are essential for preserving the quality and safety of perishable products. However, they are highly energy-intensive, with refrigeration accounting for up to 35% of energy consumption in the food industry. This demand, exacerbated by rising temperatures and increasing food needs, generates significant environmental, economic, and social challenges. While existing strategies focus on improving energy efficiency through technological advancements, such as advanced cooling systems and predictive analytics, these solutions often neglect the broader supply chain network and the influence of site-specific climatic factors. This study introduces an energy-driven supply chain network design framework that integrates logistical considerations with geographic and climatic variability to optimize the overall supply chain. Employing a mixed-integer linear programming approach, the model minimizes total costs by balancing refrigeration energy requirements and transportation ones. A testbed case study featuring a multi-echelon network in Northern Italy validates the framework. Results demonstrate significant savings, with up to 35% reductions in total costs and over 50% decreases in energy consumption in specific iterations. By incorporating energy considerations alongside logistical metrics, the proposed framework provides a holistic approach to network design, ensuring adaptability and resilience. The results highlight the potential for sustainable cold food supply chain configurations that address operational demands while promoting long-term environmental and economic sustainability.

Keywords: Decision Support System, Optimization and Control, Supply Chain Management
Abstract: This study addresses the challenge of value co-creation in the global dairy supply chain by developing a data-driven decision-support tool for selecting dairy producers. Using data from 201 Brazilian dairy professionals, the research employs Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and regression analysis to identify key selection criteria encompassing economic, production, and social factors. These criteria are integrated into an Analytic Hierarchy Process (AHP) framework to facilitate informed producer selection, promoting sustainable practices and enhancing the dairy industry's competitive advantage.

Keywords: Inventory control, production planning and scheduling, Decision Support System, Modelling Supply Chain Dynamics
Abstract: Managing perishable seafood sales in an omnichannel retail environment requires balancing customer expectations for freshness, quality, and affordability with the retailer's goals of minimizing waste, ensuring inventory turnover, and maintaining supply chain efficiency for both locally sourced and imported products. Challenges in omnichannel is heightened by the need to synchronize inventory and demand across physical and online platforms, address faster delivery requirements, and navigate constraints like real-time stock visibility, temperature-controlled logistics, and diverse customer preferences, especially for perishables. Despite the growing relevance of omnichannel strategies, there is limited literature on optimizing perishable inventory in this context. This study bridges the gap by analyzing omnichannel sales and inventory distribution of a retail chain using predictive analytics. Gradient Boosting (GB) emerged as the best-performing model for demand forecasting, achieving the lowest MAE (1.684) and MSE (7.76) on average. A mathematical model based on predicted demand is suggested for efficient inventory control. Furthermore, our analysis suggests that segmenting forecasts for online and in-store sales, accounting for day-of-week trends, can optimize inventory management, reduce waste, ensure product availability, and adhere to food safety standards, particularly for perishable seafood items.

Keywords: Robustness analysis, Industry 4.0, Robotics in manufacturing
Abstract: This research proposes a three-channel control scheme to improve transparency and stability in nonlinear bilateral telerobotic systems. The control framework emphasizes position tracking and synchronization, with an unknown input observer estimating the external force exchanged between the environment and the slave robot. This estimated force signal is incorporated into the master robot's controller to achieve the design objectives. Specifically, the control approach addresses three primary goals: (1) position tracking and synchronization; (2) enhanced transparency through accurate force reflection; and (3) stability analysis of the telerobotic system. The system architecture, structured as a three-channel bidirectional design, leverages the observer-based control law to ensure that position tracking errors remain stable. Ultimately, the performance of the proposed control scheme is validated on a nonlinear bilateral telerobotic system, demonstrating its potential to enhance both stability and transparency in real-world applications.

Keywords: Industry 4.0, Robustness analysis, Optimization and Control
Abstract: This study introduces an innovative method for enhancing the control of the OpenMANIPULATOR-X robot, integrating Fixed-Time Sliding Mode (FTSM) control with the Deep Deterministic Policy Gradient (DDPG) algorithm. The main objective of this research is to minimize positional inaccuracies and mitigate chattering that can occur during the robot’s operation. By utilizing the DDPG algorithm, a reinforcement learning (RL) agent is trained to optimize the control gains for the FTSM technique. The simulation results presented in the study highlight the effectiveness of this approach in reducing both position errors and chattering. Incorporating advanced state and reward selection in the RL framework is essential for optimizing the robot’s performance. Overall, this methodology shows promise for improving the precision and reliability of the OpenMANIPULATOR-X, particularly in uncertain environments.

Keywords: Distributed systems and multi-agents technologies, Monitoring, diagnosis and maintenance of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: In this paper, a resilient controller is designed for connected autonomous vehicles against Denial of Service (DoS) attacks. The controller utilizes a sliding mode control structure, in conjunction with an event-triggered mechanism to reduce the network burden. The stability analysis is conducted using Lyapunov theory. The proposed controller aims to maintain the stability and performance of the connected autonomous vehicle system in the presence of DoS attacks, by continuously adjusting the control inputs and by reducing the network burden by using the event-triggered mechanism. The proposed approach is applied to the steering control subsystem of Autonomous Underwater Vehicles, and the obtained results show that the proposed controller can effectively mitigate the impact of DoS attacks, reduce the network burden, and maintain the stability of the connected autonomous vehicles.

Keywords: Optimization and Control, Transportation Systems, Industry 4.0
Abstract: This paper proposes optimal control frame for trajectory planning while ensuring obstacle avoidance for specific kind of wheeled robots, which are used in manufacturing process. The proposed methodology employs a kinematic model of a wheeled robot, integrating a cost function that balances goal-reaching efficiency with safety constraints. A Control Barrier Function (CBF) is incorporated into the optimization framework to maintain the robot’s trajectory within safe regions, preventing collisions. The cost function is designed to minimize the Euclidean distance to the goal, penalize control efforts, and prioritize safety near obstacles. The control law is derived by solving a constrained optimization problem with a learning process that guarantees system stability and convergence to the goal. The Simulation results demonstrate the effectiveness of the approach in navigating complex environments with obstacles.

Keywords: Optimization and Control, Industry 4.0, Smart manufacturing systems
Abstract: Visual servoing has emerged as a promising approach for controlling robotic manipulators by leveraging visual feedback to enhance precision and adaptability. Despite significant progress, several challenges persist, including robustness to image noise, computational efficiency, and the ability to cope with dynamic environments. Visual servoing techniques are especially effective when the camera and the end effector have a limited workspace. This paper explores the use of visual servoing control in robotic systems, in particular, the comparison between imaged-based and pose-based techniques. Further, these techniques are adapted with the use of an adaptive gain in the control law to improve robustness and convergence. Experimental results for all variants are obtained from implementation on a 6DOF robotic manipulator, using an AprilTag fiducial marker.

Keywords: Modelling Supply Chain Dynamics, Production Control, Control Systems, Risk Management
Abstract: The outbreak of the COVID-19 pandemic has directly caused the infection of employees. In the post-COVID-19 era, supply chains (SCs) still face the challenge of labor shortage, due to a series of pandemics. As industry production has been significantly impacted by the uncertain labor capacity, we investigate a new SC viability building problem under labor dynamics, where the SC includes multiple suppliers and a labor-intensive manufacturer. The concerned problem comprehensively includes applying infection control measures, employing a backup supplier and establishing risk mitigation inventory (RMI) to build a viable SC. To portray the dynamics of the labor capacity, we innovatively propose a labor dynamics model based on the classic susceptible-infected-susceptible model. To hedge against disruption risks and uncertain demand, we propose a mixed-integer linear programming model integrated with optimal control methodology. Especially, our optimal control framework can dynamically fulfill SC structural reconfiguration by timely selecting appropriate suppliers to build SC viability. Then an efficient solution method is developed. Numerical experiments validate the efficiency of our algorithm.

Keywords: Supply Chain Management, Modelling Supply Chain Dynamics, Risk Management
Abstract: Severe disruptions have profound and lasting effects on the supply chain (SC) due to a triggered ripple effect that causes both temporal and structural disruption propagations. To mitigate these disruptions, it is crucial to comprehensively assess the SC risk by deeply exploring the SC performance fluctuation, which can provide support for risk mitigation strategies under long-term crises. In the literature, however, evaluating the SC risk by considering its dispersion characteristics has been ignored, especially from the risk-averse perspective. To fill the research gap, this study proposes a new approach to address a novel risk-averse multi-echelon SC risk assessment problem under data scarcity. This approach includes: (i) portraying both temporal and structural propagations along the SC via the risk-averse dynamic Bayesian network (RA-DBN) and the probability interval method (PIM); (ii) designing a novel risk-averse nonlinear programming (RA-NLP) model to formulate the problem; and (iii) developing a quadratically constrained quadratic program (QCQP) method to solve the problem efficiently.

Keywords: Supply chains and networks, Risk Management, Supply Chain Management
Abstract: Relief manufacturer within relief supply chains play a critical role in producing essential supplies to meet demands triggered by natural disasters, but they are highly vulnerable to disruption risks caused by such events. Therefore, it is essential to assess such risks under the propagation of disruptions originated from natural disasters. However, existing relevant studies neglect the temporal evolution characteristic of certain natural disasters (e.g., hurricanes) and resulting in a lack of risk assessment from a temporal perspective. To bridge this gap, this study introduces a robust dynamic Bayesian network (DBN) approach that considers structural and temporal risk propagation processes to assess the disruption risk for relief manufacturer from a worst-case perspective. The proposed approach comprises three key components: (1) modeling the propagation structure of disruption risks from hurricane events to the relief manufacturer, (2) capturing the temporal dynamics of disruption risk propagation driven by hurricane evolution, and (3) developing a worst-case DBN-based optimization model to assess the disruption risk for relief manufacturer. To demonstrate the practical applicability of the proposed robust DBN method, a case study in the United States is conducted.

Keywords: Transportation Systems, Scheduling, Operations Research
Abstract: The mobile charging station (MCS) is an innovative solution for electric vehicle charging, served as a complementary service to address the limitations of fixed charging stations (FCS), such as charging congestion at FCS and user concerns related to travel anxiety. The scheduling of MCS is crucial for its effectiveness. However, existing literature lacks comprehensive research on how the attributes of MCS impact its scheduling strategies. In practical scenarios, various types of MCS coexist with differences in attributes such as battery capacity, charging power, and energy consumption rate. These differences significantly affect their service capabilities and operational costs in satisfying electric vehicle charging demands. To address the issue, this study systematically incorporates the heterogeneous attributes of MCS into the MCS scheduling problem with a mixed fleet. A mixed integer linear programming (MILP) model is formulated with the aim of minimizing the total operational cost from the MCS operator’s perspective. A heuristic algorithm is established to solve the problem with large size.

Keywords: Operations Research, Optimisation Methods and Simulation Tools, Scheduling
Abstract: This work investigates the prediction of the quantity of fully charged batteries required at battery swapping stations for electric vehicles under demand uncertainty. Decision-making commonly follows a sequential model involving forecasting steps without feedback, referred to as an open-loop approach. For instance, during the operation of a battery swapping station, the operator uses predictive decision-making to determine which vehicles to swap in the upcoming time slot. This process helps to prepare the necessary number of fully charged batteries. A discrepancy, such as an insufficient number of charged batteries for swapping, leads to increased costs for the station to meet current demand. To address this, we adopt a closed-loop framework. Within this framework, the optimal prediction model is determined based on an application-specific cost function. We structure the estimation method as a bilevel optimization problem. This problem is solved using an exact method that relies on the KKT conditions of the second-level problem. Finally, the case study demonstrates that the closed-loop prediction approach for the battery swapping station operation system outperforms the traditional open-loop method, which is least squares.

Keywords: Scheduling, Production planning and scheduling, Heuristic and Metaheuristics
Abstract: Pallet automation systems (PASs) have gained more and more attention from many manufacturing enterprises with the development of flexible automation, where machines, fixtures, and pallets are three critical resources. Few scholars study flexible manufacturing scheduling considering fixture and pallet resources. Meanwhile, they ignore the loading and unloading time, potentially leading to an extended makespan. Therefore, this paper presents a machine-fixture-pallet resources constrained flexible pallet automation system scheduling considering loading and unloading time (FPAS). A mixed-integer programming model is established to minimize makespan. Considering the mutual constraints among resources, a new decoding method to choose resources for minimizing delay time (MDT) is proposed. An improved adaptive large neighborhood search algorithm (IALNS) with the simulated annealing method for local search is applied. The case study illustrates that IALNS with MDT rule (IALNS-MDT) can effectively reduce makespan while MDT enhances the quality of initial solutions.

Keywords: Scheduling, Optimisation Methods and Simulation Tools, Business Process Modeling
Abstract: Outpatient appointment scheduling is complicated by uncertainties such as patient no-shows and variability in service times, leading to under-utilisation of resources and patient dissatisfaction. While overbooking strategies mitigate the impact of no-shows, achieving an optimal balance between resource optimisation and patient satisfaction remains challenging. In this study, we propose a novel approach to outpatient appointment scheduling by applying the newsvendor problem framework to manage overbooking and reduce the negative effects of no-shows. Our model optimizes the overbooking threshold to maximize expected profit while balancing resource utilisation and patient satisfaction, considering service time variability. A sensitivity analysis highlights how key parameters influence the optimal overbooking level, providing a quantitative solution to balance patient waiting times and physician idle times. Our findings offer actionable insights for healthcare providers seeking to improve clinic efficiency.

Keywords: Scheduling, Industrial and applied mathematics for production, Operations Research
Abstract: Nowadays, process automation and the requirement for simultaneous decision making in supply chain processes have driven the evolution of production planning and scheduling models. One of the problems best known for its complexity and real-industry use is the Flexible Job Shop Problem. The difficulty of planning preventive maintenance and fitting it in the production schedule in the optimal way requires the creation of mathematical models capable of describing the problem and solving it. Machine setup times make the problem more realistic, given that setup times are in fact different from the time taken by the machines to process the jobs. In this paper, an Integer Linear Programming model is presented to describe the problem and solve short instances while a Variable Neighborhood Descent algorithm is used to solve large instances. To test the performance of this model, some instances are created using random numbers. The results show that the mathematical model finds the optimal solution for the majority of the instances and the results of the large instances are reported to be used as references for future work.

Keywords: Production planning and scheduling, Heuristic and Metaheuristics, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Recently, integrated optimization of packing and scheduling have gained increasing concerns in manufacturing systems because of their interdependence. In this study, the integration of two-dimensional rectangular packing and flexible job-shop scheduling is addressed as a bilevel optimization problem, where the lower level task of packing serves as a constraint for the upper level task of scheduling. To tackle it, a novel algorithmic framework combining evolutionary algorithms and meta-reinforcement learning, named EA-MRL, is proposed. In EA-MRL, the evolutionary algorithm is employed for optimizing the scheduling task and a model-agnostic meta-learning-based Q-learning algorithm is developed to solve the packing task. Through performing experiments on 21 synthetic datasets, the effectiveness of EA-MRL has been verified.

Keywords: Scheduling
Abstract: In this paper, we focus on scheduling problems in general, and on how to model the constraints typically encountered in these problems using Hexaly's modeling formalism. Hexaly is a global mathematical solver offering a rich nonlinear and set-based modeling formalism. We show how we can use its collection of decision variables (lists, intervals, and optional intervals) and operators (set-based operators, variadic operators, lambda functions) can be used to efficiently model characteristics such as disjunctive and cumulative resources, preemption, or multi-alternatives. We also show that its compact models allow to solver to reach very good performance on scheduling problems.

Keywords: Human-Automation Integration, Industry 4.0, Decision-support for human operators
Abstract: The growing adoption of AI in industry calls for a reevaluation of task allocation between humans and technology. While AI enhances human and technical capabilities, it also introduces risks, such as trust issues and potential harm to individuals, organizations, and the environment. Research on the dynamic allocation of shared tasks in industry, particularly involving AI, remains limited. This paper highlights the need for a broader perspective on task allocation, emphasizing human agency, control, and risk-based approaches. It explores the dynamic allocation of shared tasks in six human-AI teaming use cases in construction and manufacturing.

Keywords: Supply Chain Management
Abstract: Logistics companies and their personnel experience multiple challenges. Orders need to be processed quickly with a low margin of error. The repetitive character of the job and the required accuracy make the order-picking job unattractive. However, humans remain relevant in the order-picking process. This study focuses on the psychosocial aspects of human factors, which include non-physical work dimensions. Leadership is an important psychosocial factor impacting employee well-being in warehouses. By using a case study approach, this research explores how order pickers experience the impact of leadership behaviour on their well-being at work and how the gender of the leader plays a role in this context. The data collection of this ongoing study will be finalised at the beginning of 2025.

Keywords: Design and reconfiguration of manufacturing systems, Decision Support System, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: In today’s dynamic market environment, adapting production systems to meet diverse and evolving requirements is crucial. Traditional single-model mass production has transitioned to mixed-model mass customization, demanding flexibility and efficiency without compromising productivity. This study compares two advanced assembly systems: the Fishbone Assembly Line (FAL) and the Walking Worker Assembly Line (WWAL), focusing on their ability to balance worker efficiency, system flexibility, and productivity. The FAL features a central backbone with fixed workers and buffers, allowing products to overtake and move independently, optimizing operator utilization. Conversely, the WWAL employs a U-shaped layout where workers sequentially complete tasks on a single product, with flexibility provided by parallel workstations and worker mobility, albeit with potential idle times. A MATLAB simulation was employed to conduct a parametric analysis, systematically examining key factors such as buffer presence, parallel workstations, time imbalances, and worker efficiency. Under baseline conditions—two parallel workstations, no buffers, and equal operator numbers—both systems exhibit equivalent productivity, with FAL achieving higher operator utilization due to its static task allocation. However, as the parameters change, the WWAL outperforms FAL when flexibility is prioritized, especially with excess workstations compared to operators. This research offers actionable insights for optimizing assembly line configurations in manufacturing, guiding decision-makers in selecting systems tailored to specific production requirements. Further quantitative results will be presented to clarify the interplay between layout, configuration, worker efficiency, and system performance.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Human-Automation Integration, Industry 4.0
Abstract: Work-related musculoskeletal disorders are a leading cause of industrial injuries, with manual material handling (MMH) contributing to over 500,000 cases annually in the U.S. This study supports the development of a human digital twin (HDT) by enabling accurate human activity recognition for meaningful analysis. Using computer vision-based OpenPose for pose estimation, we classify two of the fundamental MMH moves, lifting and lowering, with Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. LSTM proved more robust, achieving 92.2% accuracy. This approach demonstrates the feasibility of noninvasive MMH monitoring, enabling real-time safety assessments and enhancing HDT-based industrial applications.

Keywords: Human-Automation Integration, Modeling, simulation, control and monitoring of manufacturing processes, Optimisation Methods and Simulation Tools
Abstract: The integration of human factors and ergonomics (HF/E) into industrial and operational decision support modeling has grown rapidly over the past decade. In particular, physical aspects (e.g., physical workload, fatigue-recovery cycles) have been popular in developing human-centered solutions in operations management (OM). These solutions aim, first, to prevent both short- and long-term health issues among workers (e.g., work overload and occupational musculoskeletal disorders) and, second, to enhance the system performance of model-based solutions in real-world settings. However, adopting a human-centric perspective necessitates interdisciplinary knowledge. Specifically, each model or tool developed by ergonomists possesses unique characteristics (including the original experimental settings, the scope of collected data, and the intended application scenarios). They should thus be used with caution in managerial decision support models. To facilitate the knowledge transfer from HF/E to OM, this pilot study provides preliminary results of: (i) a scoping review of the integration of physical HF into decision support modeling in operations management; (ii) a critical evaluation of model assumptions and the interpretation of results from an HF/E perspective; and (iii) the development of a structural framework to suggest HF/E model choices. The current study presents preliminary results with an interdisciplinary perspective, which will be extended in future research.

Keywords: Line Design and Balancing
Abstract: This paper deals with the design of a manual assembly line on which collaborative robots (cobots) and humans work together. In the cobot-human assembly line design problem, in addition to assigning tasks to the stations, selecting the processing alternatives for the tasks and allocating the workers and cobots to the stations, the scheduling of the tasks is also required due to the different collaboration types. We define the following collaboration types: (1) Independent: Workers and cobots work on separate tasks. Each task is solely done by either the human or the cobot; (2) Concurrent: A worker and a cobot perform together the same task (e.g., the cobot holds the part while the human makes some operation); (3) Sequential: Two tasks, one done by the worker (cobot) and the other by the cobot (worker) have to be done continuously one after the other.

In our human-cobot assembly line design problem, we also aim to determine the line configuration. Instead of assuming a given configuration such as a station includes one worker and can accommodate up to one cobot or a cobot can work with two adjacent stations, we regard the term module and define the assembly line as a sequence of modules. A module is a segment of the line that contains a set of resources that collaborate during the cycle time. A module includes at least one station and at most one cobot. Considering the cobots can perform different tasks in two or three adjacent stations, the modules can include up to three stations and workers.

Given the number of modules, stations, workers and cobots, we formulate the human-cobot assembly line design problem with the objective of minimizing cycle time as a mixed integer linear programming (MILP) formulation. To improve the performance of the formulation, we also develop some bounds on the cycle time. We present the results of our preliminary computational analysis where a commercial solver is used to solve the test instances from the literature. Our computational analysis also includes a comparison to the other relevant MILP formulations available in the literature by simply assuming the configuration is given for each module as consisting of one station with at most one worker and/or one cobot.

Keywords: Design and reconfiguration of manufacturing systems, Line Design and Balancing, Decision Support System
Abstract: Temporary production systems, such as ephemeral factories, are gaining traction in offsite construction due to their flexibility and efficiency. This paper focuses on optimizing assembly line configuration in Construction Ephemeral Factories (CEFs), with a particular emphasis on ergonomic considerations and dynamic adaptability. Through a review of relevant literature, we identify key principles and propose a framework that enhances assembly processes by integrating ergonomic risk management and strategies for adapting to changes in demand or workforce availability. The main contribution of this paper is the development of a framework for assembly line configuration in ephemeral factories, incorporating human factors across different phases and time horizons of the factory lifecycle.

Keywords: Line Design and Balancing, Production Control, Control Systems, Industry 4.0
Abstract: In an assembly line, a product passes through different stations, undergoing every task of the precedence graph that describes its assembly process, until it reaches the state of a finished product. Modeling the occupational (ergonomic) and mental workload of assigned tasks requires careful control of task sequencing at each station. From the operators’ point of view, the task sequence repeats with period equal to the cycle time. The operator that is assigned to a station performs only a subset of the total tasks, repeating it in a loop on every product that flows in the line. We present a new model that accurately considers these loops, integrating pauses between physically or mentally demanding tasks, or inserting alternative tasks as a way to rest. The result is a tool that places the human at the center of the production systems, in line with Industry 5.0 indications and mitigating the risks of workforce shortages.

Keywords: Line Design and Balancing, Design and reconfiguration of manufacturing systems, Operations Research
Abstract: To tackle the complexities of production assembly line optimization, we present a mixed-integer programming formulation for production assembly line optimization, minimizing costs of workstation construction, task-specific equipment, and times buffer while ensuring production demands are met. A key focus of this research is to achieve a balanced workload among workstations, which is complicated by the variability in workstation reliability characterized by Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR). We address this challenge by developing a mathematical model that incorporates task precedence relationships and actual cycle time constraints. We validate our proposed method through numerical experiments using real-world datasets, demonstrating considerable practical benefits for complex production line. The result demonstrates efficiency improvements and cost reductions, which enhance the overall performance of the assembly line. The proposed model incorporates the impact of machine failures, integrates time buffers, and accommodates the requirements of various tasks, contributing to production optimization. It offers practical guidance for practitioners seeking to improve production efficiency. Furthermore, we introduce the Precedence-Driven Task Grouping (PDTG) method, which enhances the traditional Ranked Positional Weight (RPW) method by offering wider flexibility in task assignment. This approach reduces the number of workstations, improves task allocation efficiency, and minimizes idle time.

Keywords: Line Design and Balancing, Robotics in manufacturing, Industry 4.0
Abstract: The integration of collaborative robots (cobots) in the assembly line balancing problem (ALBP) represents a challenging opportunity to perform strategic task assignments to workstations targeting both assembly line efficiency and worker satisfaction. Cobots are designed to accomplish the progression of repetitive or hazardous tasks, allowing workers to dedicate more attention to valuable assembly activities that require non-replicable skills and human dexterity. Deploying human-robot collaboration (HRC) in ALBP often aims at increasing system performance as its primary objective; however, multi-objective models have started to spread in literature considering both economic, social, and sustainable targets, demonstrating compliance with Environmental, Social, and Governance (ESG) paradigm and Industry 5.0 principles. This study proposes a bi-objective mixed-integer nonlinear programming (MINLP) mathematical model to simultaneously minimize cycle time and the percentage of non-value-added ratio. In particular, the algorithm developed targets the workstation that exhibits the greatest cycle time in the ALBP solution, thereby constraining the productivity of the assembly line. Maximizing value-added task assignments to workers does not only imply reducing the strenuous workload and hazardous task progression but also favoring the progression of assembly activities that can increase motivation and morale of workers due to the high skills and non-replicable competences required for their accomplishment. The proposed model is applied to a numerical test case on an experimental dataset to provide preliminary results for the HRC-ALBP.

Keywords: Industry 4.0, Business Process Modeling, Inventory control, production planning and scheduling
Abstract: Given the relevance of warehouse sustainability in the realm of the Logistics 5.0 paradigm, this study evaluates the environmental and economic impacts of green warehousing (GW) measures and provides a practical application to a real business case. A discrete-event simulation approach is applied to the IKEA distribution centre located in Northern Italy to assess strategies for optimizing energy self-consumption from on-site photovoltaic (PV) systems. Three scenarios are identified and investigated: substituting the current mobile material handling equipment (mMHE) fleet with forklifts fully powered by Li-Ion batteries charged through opportunity charging, replacing old PV panels with high-efficiency models, and the combination of both measures. Results are provided in terms of energy, environmental and economic impact, and demonstrate that implementing a Li-Ion mMHE fleet improves energy efficiency and environmental sustainability, achieving a return on investment (ROI) of 26% with a payback period (PBP) of 4 years. Additionally, replacing PV panels increases renewable energy generation by 47%, although some inefficiencies were noted due to on-site energy demand/supply mismatch. Finally, the integration of both GW measures significantly enhances energy and environmental performance, leading to savings that exceed those observed in both scenarios previously evaluated. Findings are discussed and future research directions are outlined

Keywords: Supply Chain Management, Facility planning and materials handling, Human-Automation Integration
Abstract: Even though there is a growing interest in Industry 5.0 (I5.0), holistic studies assessing the technologies and managerial approaches driving its adoption—especially regarding their impact on intra-logistics systems—are lacking. Based on Design Research Methodology, this paper proposes a framework to evaluate how technologies and approaches related to the three key dimensions of I5.0—human-centricity, resilience, and sustainability—affect the design and performance parameters of material handling, storage, and picking systems. Using methods like a Systematic Literature Review and expert surveys, the study pinpoints key technologies and approaches for I5.0, along with the primary factors influencing the design of intra-logistics systems and their operational and economic performance. An assessment framework has been developed relying on Domain Mapping Matrices. Further research will include a Delphi study with industrial experts to determine the most impactful I5.0 technologies and approaches on intra-logistics systems. In such a way, guidelines for the development of these systems can be derived.

Keywords: Human-Automation Integration, Robotics in manufacturing, Facility planning and materials handling
Abstract: Warehousing plays a crucial role in modern supply chains, and picking activities, essential for warehouse operations, remain heavily reliant on human operators, often exposed to repetitive tasks and physical strain, increasing the risk of work-related musculoskeletal disorders (WMSDs). In this context, assistive technologies such as exoskeletons have emerged as promising solutions to enhance operator well-being and efficiency. This study investigates the impact of active exoskeletons on human operators and performances in parts-to-picker tasks. Using bio-signals (EMG and EEG), self-reported metrics, and productivity measures, the study evaluates impacts on human factors, i.e., on physical, cognitive, and perceived workloads, as well as on picking productivity, under varying operational conditions, finding a potential reduction of 23% of maximum muscular activity with active exoskeleton, associated to a possible reduction of productivity. The study contributes to the academic and practical understanding of assistive technologies in warehousing, offering insights into human factors and technology interplay. Copyright © 2025 The Authors. This is an open-access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Keywords: Smart transportation, Supply Chain Management, Optimization and Control
Abstract: This study examines the integration of Internet of Things (IoT) and Green Vehicle Routing Problems (GVRP) to create modern, eco-friendly fleet management system that overcomes the limitations of traditional system through real-time data analytics, predictive maintenance, and optimized decision-making. While existing research on GVRP focuses on theoretical route optimizations, reductions in fuel consumption and greenhouse gas emissions, incorporating real-time IoT data can further aid in measuring these impacts accurately, and understanding their combined potential. In this novel study, the proposed system integrates Teltonika FMC003 telematics device, cloud computing, and GVRP algorithms to promote sustainable and cost-effective advanced fleet management.

Keywords: Industry 4.0, Sustainable Manufacturing, Supply chains and networks
Abstract: This review paper proposes a research agenda for integrating sustainable, human-centric public lighting systems in urban environments. It addresses the ecological and societal impacts of artificial light at night, aligning public lighting with Logistics 5.0 principles. Given the emerging nature of Logistics 5.0 and its limited application to public lighting, we conduct a scoping literature review of Industry and Logistics 5.0 in this context. Key research directions include developing IoT-enabled adaptive lighting technologies, strategies for ecological sustainability, and stakeholder collaboration. This paper aims to provide actionable insights for designing public lighting systems that balance energy efficiency, biodiversity preservation, and human well-being.

Keywords: Supply Chain Management, Modelling Supply Chain Dynamics, Robustness analysis
Abstract: In uncertain, hyper-connected and fast-changing environments, supply chains are becoming more prone to disruptions and unexpected events which affect their stability and ultimately the future consumer demand. Traditional time series forecasting approaches fail to take into consideration environmental uncertainties related to the volatile nature of the supply chains. This paper provides an evaluation of the robustness of time series forecast models under disruptions and future uncertainties. By introducing disruption scenarios with varying intensities and durations, the study evaluates the robustness of forecast models using a range of statistical and machine learning models. This study was evaluated on M3 monthly data to test the performance of forecast models and selection strategies using various accuracy metrics. The results underscore the need for a new robust forecast model selection approach to find a trade-off between accuracy and robustness to future uncertainties.

Keywords: Supply Chain Management, Operations Research, Decision Support System
Abstract: This paper addresses the problem of optimising the spatial and temporal distribution and allocation of vaccines among different groups in an epidemic situation that minimizes the sourcing and shipping costs and epidemiological impacts (e.g., infections or deaths). The problem combines a combinatorial distribution model with a discrete-time, continuous-state Susceptible-Exposed-Infected-Removed (SEIR) compartmental model that governs the disease dynamics, resulting in an intractable mixed-integer bilinear program. We propose a novel exact logic-based Benders decomposition algorithm to solve the problem. Furthermore, a simulation procedure is developed to solve the special case of uniform mixing. Our numerical analysis demonstrates the effectiveness of the proposed model and solution algorithm.

Keywords: Inventory control, production planning and scheduling, Industry 4.0, Supply chains and networks
Abstract: Efficient inventory management is crucial for ensuring financial stability and operational efficiency, especially in dynamic supply chains characterized by demand variability and logistical complexity. Reinforcement Learning (RL), a branch of Artificial Intelligence, has emerged as a promising approach to optimize inventory control in uncertain environments. Unlike traditional inventory management techniques, RL dynamically learns decision-making strategies through interaction with the environment, adjusting stock levels based on real-time feedback. This systematic review examines the application of RL in finished goods inventory management by analyzing 57 relevant studies. The findings highlight RL’s potential to enhance demand forecasting, reduce stockouts and excess inventory, and improve overall supply chain resilience. However, practical challenges such as computational complexity, data-sharing limitations, and integration with legacy systems hinder widespread adoption. The review identifies key research opportunities, including the integration of RL with emerging technologies such as IoT, blockchain, and digital twins, as well as the development of hybrid models that combine RL with traditional improvement methods. These insights contribute to advancing the field, offering a roadmap for future research and industrial applications.

Keywords: Smart manufacturing systems, Industry 4.0, Scheduling
Abstract: The optimization of setup times in manufacturing is important to increasing production efficiency and flexibility, especially in highly dynamic environments. Traditional Advanced Planning and Scheduling (APS) systems often rely on static optimization techniques, limiting their effectiveness in handling complex and variable setup processes. This systematic review explores the integration of Machine Learning (ML) algorithms into APS tools to enhance setup time optimization. By analyzing recent studies, we identify key ML approaches, their impact on production scheduling, and the main challenges faced during implementation. The findings highlight that ML-driven APS solutions can achieve setup time reductions of up to 35%, particularly in discrete manufacturing industries. However, challenges related to data quality, system integration, and computational complexity must be addressed to fully leverage these technologies. This review provides a structured framework for the application of ML in APS, bridging the gap between theory and industrial practice, and outlining future research directions for enhancing setup time optimization in manufacturing.

Keywords: Quality management, Decision Support System, Smart manufacturing systems
Abstract: This paper presents the concept for a smart assistance system designed to evaluate and enhance data quality in manufacturing SMEs, enabling the effective use of data analytics, particularly predictive analytics. The system integrates a maturity model, a methods catalog, and an e-learning platform to support SMEs in assessing data quality and selecting appropriate data analytics methods for smart manufacturing. A chatbot guides users through the assessment and provides recommendations. The proposed solution addresses key challenges for SMEs, such as insufficient expertise and limited resources, fostering a data-driven culture in manufacturing. The functionality of the tool is discussed in a user story, and its feasibility and adaptability for broader applications are demonstrated.

Keywords: Sustainable Manufacturing, Decision Support System, Industry 4.0
Abstract: This paper aims to provide a methodology for value-driven management (VDM) and stress its centrality in increasing organisations’ operating performance, strategic fit, and stakeholders’ involvement. Existing VDM frameworks primarily prepared for the large enterprises overlooking the resource constraints and structural limitations of SMEs. Hence, the research unfolds this concern of applying VDM through empirical findings. This outlines two operational strategies comprising of reactive and proactive which help organisations to be able to operate in the dynamic market environment. The relationship between technology adoption and value realisation is discussed with an attempt to present that technology is not a solution itself but a means of creating value. Furthermore, the paper also explores the relationship between value chains and business ecosystems and the need for their application in enhancing the flow of operation strategies. Thus, presenting the Value-Driven Assessment Methodology designed for SMEs this work outlines three phases: scoping, emerging design, and concept development disapproval of using only quantitative and qualitative assessments. In doing so, this research can help better understand how VDM can be positively impacted and practiced in smaller organisations leading to enhanced capabilities of creating sustainable competitive advantage and business success.

Keywords: Sustainable Manufacturing, Industry 4.0, Decision-support for human operators
Abstract: Remanufacturing extends product lifecycles and reduces waste, but its success depends on effective information exchange throughout the value chain. This article evaluates the impact of Digital Product Passport (DPP) on the efficiency of remanufacturing from operational, environmental, and social perspectives. A DPP integrated with a digital thread architecture was tested in a controlled experiment across two laboratories, simulating a real-world remanufacturing scenario. The study compared two approaches: one without DPP and one utilizing DPP for improved decision-making. The results showed that the DPP significantly reduced the remanufacturing cycle time by minimizing the variability in quality control. However, there are potential negative effects on environmental and social metrics. While DPPs enhance operational efficiency, further investigation is needed to understand their broader implications and establish protocols for their sustainable use.

Keywords: Industry 4.0, Production Control, Control Systems, Smart manufacturing systems
Abstract: Manufacturing companies have progressed in the adoption of digital technologies to monitor assets and daily activities. However, there remain challenges for SMEs when implementing these technologies due to initial investment costs and insufficient digital workforce. This work reports how low-cost digital solutions based on the so-called Shoestring approach were successfully deployed across 110 manufacturing SMEs through an Innovate UK supported project. This involved applying a structured deployment process to boost technology adoption, reducing barriers and guiding manufacturers to start their digitalisation journey while providing business benefits from data insights into their operations and customers.

Keywords: Sustainable Manufacturing, Fuzzy logic control, Industry 4.0
Abstract: This study explores the strategic selection of environmental indicators for the precast concrete industry using the Fuzzy Delphi Analytic Hierarchy Process (FAHP) method. A systematic literature review (SLR) identified 97 sustainability performance indicators in manufacturing, narrowing down to 39 environmental ones. Expert consultations finalized 27 indicators relevant to the sector, categorized under energy, water, air pollution, soil, and waste. Energy indicators, like reducing consumption and improving efficiency, ranked highest, reflecting their importance in energy-intensive processes such as curing. The results reveal significant gaps in air and soil indicators, often deprioritized in sustainability assessments. These indicators support compliance with Corporate Sustainability Reporting Directive (CSRD) standards and inform operational and strategic decisions. Integration into Enterprise Resource Planning (ERP) systems enhances sustainability reporting and performance tracking. In Industry 4.0 (I4.0), this enables advanced, data-driven sustainability approaches, providing valuable insights for stakeholders and aiding decision-making.

Keywords: Industry 4.0, Smart manufacturing systems
Abstract: Radio Frequency Identification (RFID) technology is an important tool for industry 4.0. It enables identification, management of assets and tracking throughout the supply chain. In this paper, we explore the use of RFID technology in scenarios involving close proximity,< 40mm, interactions at high speeds, up to 8.93ms . We aim to test and validate the success rates of RFID systems under challenging conditions, focusing on factors that influence performance such as water barrier, frequency and speed during passes. The results show that the high-frequency readers demonstrated a better ability to successfully read tags moving at higher speeds, reading the tag 100% of the passes at speeds up to 2.40ms , at which point the low-frequency readers never managed to read a tag.

Keywords: Transportation Systems, Modeling, simulation, control and monitoring of manufacturing processes, Decision Support System
Abstract: This paper explores the application of a hybrid simulation methodology—integrating agent-based and discrete-event simulation—to enhance reverse logistics (RL) processes. Reverse logistics is increasingly critical in circular economy strategies, enabling the recovery, remanufacturing, and recycling of end-of-life products while minimizing environmental impacts. Within the context of the Sustainable Intelligent Industrial Planning (SIIP) project, financed by the Italian government, this study develops and applies a multi-method simulation framework to model and evaluate RL supply chains using real-world data provided by an industrial partner. The framework supports the analysis of economic and environmental trade-offs across different supply chain configurations, specifically considering direct transportation and transportation to a storage centre before final transportation. Simulation results reveal significant cost-emission trade-offs: while introducing a storage centre reduces CO₂ emissions by optimizing transportation routes, it incurs higher operational costs. The findings emphasize the need for context-specific decision-making to balance sustainability goals with economic efficiency, demonstrating the potential of hybrid simulation tools to inform strategic planning in RL systems.

Keywords: Facility planning and materials handling, Supply Chain Management, Discrete event systems in manufacturing
Abstract: The paper presents an analysis of the advantages and requirements of Digital Twins (DTs) in the field of logistics. The article provides illustrative examples of the potential applications of DTs and demonstrates how information from the operational environment can be utilised. The DTs presented are designed to assist in the decision-making processes associated with the control and operation of logistics systems. In contrast with the approaches presented in the literature, only limited data is available in operational information systems, given that the widespread use of IoT sensors is not yet widespread. The analysis of three case studies has led to the proposal of a layer model for the architecture of DTs. This model comprises four functional layers, which are used by two overarching layers (backbones) for data storage and workflow management. The information display and user interface are realised via a front-end layer. The proposed layer model is intended to support future development and implementation, and to simplify the functional division and exchange of information between the DT functions.

Keywords: Transportation Systems, Smart transportation, Robotics in manufacturing
Abstract: This paper demonstrates the feasibility of an AI-based calibration method for enhancing the positioning accuracy of Autonomous Mobile Robots (AMR) by using a motion capture system. While similar calibrations have been tested on other robot platforms, this work explores their applicability to AMRs. AMRs play a critical role in material transport between workstations, necessitating precise positioning at transfer stations to minimize errors and maximize process stability. By employing a highly accurate external measurement system, specifically an optical motion capture setup, this study aims to automatically calibrate the AMR’s positioning system. Previous calibration methods remain largely manual with the need of human interaction in the calibration process. This approach aims to automate the calibration by reducing the needed interactions. To achieve this a machine learning algorithm is trained on the discrepancies between the actual and intended positions, allowing for real-time adjustments and improved accuracy. Experimental results demonstrate a 20% improvement in positional accuracy, demonstrating that the approach is viable for AMRs.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Decision Support System
Abstract: Piezoelectric actuators are essential for high-precision microassembly. However, monitoring their health state presents significant challenges due to their compact size and the inherent complexity of their modeling. This study presents a machine learning-based approach to predict failures in a micromanipulation system with four piezoelectric actuators. Log data was used to extract features, and autoencoders were trained on healthy-state data to detect deviations signaling faults. The method was validated using real-world training data and simulated failure scenarios, successfully distinguishing between healthy and faulty states. This approach offers a promising solution for monitoring the health of piezoelectric actuators in precision systems.

Keywords: Smart manufacturing systems, Optimization and Control, Production planning and scheduling
Abstract: Optimizing resource allocation in dynamic production environments, particularly in Hybrid Flow-Shops (HFS), remains a challenging task. Building on prior work that combined Genetic Algorithms (GA) and Machine Learning (ML) to predict near-optimal allocations (Hamiti et al. (2024)), this study identifies and addresses an issue termed the "partial enumeration problem". This problem arises from impossibility to represent different solutions with equal qualities during data generation, leading to reduced ML models performances. By formalizing the concept, categorizing it, and developing algorithms to detect and mitigate these inconsistencies, by refining training data to account for this issue, this work lays the foundation for future ML models capable of improving decision-making in dynamic and flexible manufacturing systems.

Keywords: Sustainable Manufacturing, Smart manufacturing systems, Supply chains and networks
Abstract: The absence of cohesive, technology-aware frameworks for energy-efficient building retrofits leads to inefficiencies, jeopardizing both corporate success and energy and emission reduction targets. Our end-to-end, all-stakeholder framework leverages advanced technologies, such as precise structure and performance modeling techniques, optimization and simulation methods, and digital twins, seamlessly integrated into a unified process supported by sophisticated data management. This holistic approach unleashes the synergistic technological potential throughout the retrofit project, significantly improving the inherent decision-making processes. Additionally, we introduce a conceptual collaborative digital twin ecosystem for the generation of knowledge in retrofitting and building automation systems, and pinpoint areas for further research.

Keywords: Sustainable Manufacturing, Operations Research, Scheduling
Abstract: Abstract: The global energy transition towards sustainable and decentralized energy systems has highlighted the importance of optimizing Local Energy Markets (LEM). This paper presents a mathematical model to solve energy trading problem between the nodes in a microgrid in a LEM with an objective function of maximizing the social welfare of the microgrid. In this model, we developed a utility function that represents the willingness of the batteries to sell their energy, promoting their involvement in the LEM. Additionally, we proposed a pricing method that differentiates between various types of prices (internal prices, battery prices, and main grid prices) to encourage nodes to participate in the LEM rather than relying on the main grid. Results indicate that the integration of batteries into the LEM reduces energy costs for each node and ensures self-sufficiency.

Keywords: Operations Research, Probabilistic & statistical models in industrial plant control, Decision-support for human operators
Abstract: The optimal power flow problem with demand uncertainty is seldomly used. We propose a two-stage quadratic stochastic programming with fixed recourse for the optimal power flow with uncertain demand and solve it with a specialized interior point method. We establish a demand probability distribution approximation, based on the Brazilian consumption data, and obtain a good performance from the proposed probability distribution. Numerical tests are performed on Brazilian and IEEE test systems. The implementation achieves convergence for all tested systems and obtain a positive stochastic value solution for all tests, indicating that it is advantageous to consider the stochastic solution.

Keywords: Supply Chain Management, Modelling Supply Chain Dynamics
Abstract: The study investigates how to improve supply chain and logistics sustainability in Oman's oil and gas industry through economic, environmental, and social perspectives. The research tackles industry-specific obstacles while staying in line with Oman’s Vision 2040 through the implementation of circular economy strategies including waste reduction and resource reuse as well as recycling. The research combines quantitative and qualitative methods to deliver actionable insights and customized strategies that improve sustainable logistics systems. Organizations can use the proposed framework to track and improve supply chain practices which will create a sustainable and resilient energy industry in Oman.The primary aim of this research is to develop a robust framework for measuring and improving the sustainability of supply chain and logistics operations in Oman's thriving oil and gas sector. The framework focuses to measure and improve sustainability through evaluations of environmental impact and resource efficiency along with social responsibilities and incorporates advanced technologies such as IoT, blockchain, and data analytics.

Keywords: Supply chains and networks, Industry 4.0, Optimization and Control
Abstract: This study examines the mutual dependencies between Digital Supply Chains (DSCs) and Digital Supply Chain Twins (DSCTs) within the context of technological transformation and logistics networks evolution. We conducted a systematic literature review to align the development stages of DSCTs and DSCs using maturity models, identifying commonalities in their evolution. While DSCTs are widely recognized as a cornerstone technology for resilient DSCs, their implementation in practice remains limited. The synthesis results in a unified six-stage maturity model, aligning the parallel evolutions of DSCs and DSCTs, and demonstrating their interdependence at each level of development.

Keywords: Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes, Smart manufacturing systems
Abstract: Technological advances in Industry 4.0 pave the way for mass personalization. Indeed, technologies like the Internet of Things, artificial intelligence, and autonomous robots help optimize production processes, reduce labor, and increase manufacturing flexibility, thus enabling customers to order personalized products at lower costs. Consequently, the order-driven production (ODP) strategy has evolved. It now considers not only the quantity of products in each production order but also the characteristics of the product unit. A manufacturing system adaptable to characteristic changes of different product units must be able to add, remove, and replace production equipment quickly and automatically, with no negative impact on its current production process. This ability is also known as Plug and Produce (PnP). The main challenge in PnP lies in the heterogeneity of production equipment, which differs in features, functions, and communication protocols. Asset Administration Shell (AAS), the industrial-grade standard emerging in Industry 4.0, implies an efficient approach to addressing this challenge using AAS digital twins. Unfortunately, it lacks sufficient detail regarding ODP. Thus, this paper proposes a new architecture, named XAAS, which relies on the AAS standard and extends it with additional features and services to address PnP and ODP.