Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Simulation technologies
Abstract: Twin-screw wet granulation (TSG) enables continuous particle size enlargement in the manufacturing of solid dosage form pharmaceutics. After wet granulation, a drying step is required, which is realized in the vibrated fluidized bed dryer (VFBD) of the QbCon® 1 lab scale continuous production line. The product’s moisture content and temperature are the critical quality attributes (CQAs) of the drying process. This paper presents an extension for granule temperature to the moisture content model proposed by Elkhashap et al. (2020a). The combined granule temperature and moisture content model can predict both CQAs and gives a physical, quantitative relation between the two values. A full factorial experimental design, varying drying air temperature, air flow, and vibration acceleration was performed on the real VFBD to validate the model. The model shows an accurate prediction for the temperature (RMSE = 4.4 K) and for the moisture content (RMSE = 0.021) even with limited amounts of training data present. This model can be used in future work for process optimization, monitoring, and control.

Keywords: Design and reconfiguration of manufacturing systems, Facility planning and materials handling, Optimisation Methods and Simulation Tools
Abstract: This paper presents a novel approach to layout design for Reconfigurable Manufacturing Systems, focusing on optimizing the physical arrangement of machines and resources to enable rapid, cost-effective adjustments for varying production needs. Using a new Mixed Integer Linear Programming Model, the approach targets Reconfigurable Machine Tools to enhance flexibility in response to fluctuating product types and volumes. A case study is developed and analyzed using the proposed model, followed by validation through a simulation approach. Simulation results provide insights into model behavior and enable solution refinement, particularly in optimizing resource utilization and average waiting time of products in the system.

Keywords: Production planning and scheduling, Optimisation Methods and Simulation Tools, Pricing and outsourcing
Abstract: This paper introduces I4Evosim, a gamified simulation of a competitive engineer-toorder (ETO) market. I4Evosim was developed to teach students about two complementary aspects: the ETO context and the inherent uncertainty of its products, as well as optimization approaches for planning and scheduling. The simulation encourages students to design a business strategy in a competitive market and conduct a retrospective strategic performance analysis. Gamification mechanisms make it easier to grasp a complex subject that combines diverse and uncertain decisions, constraints, and objectives. The findings of a preliminary experiment conducted with students at Université Laval allowed us to measure the platform’s impact on the learning process.

Keywords: Design and reconfiguration of manufacturing systems, Line Design and Balancing
Abstract: The demands on assembly systems are continuously increasing. Beyond the necessity to assemble highly complex products cost-effectively and with high quality in ever shorter time frames, aspects such as sustainability and resilience are gaining more importance, further intensifying the challenges in planning and its support. This paper presents a new ontology-based framework that combines an planning methodology, an ontology, and modular solution components to address these challenges. A prototype support system, developed based on this framework, was validated through an industrial case study, demonstrating its effectiveness in facilitating the rough planning phase of assembly systems. The results show significant improvements in decision-making processes within assembly system planning.

Keywords: Inventory control, production planning and scheduling, Smart transportation, Operations Research
Abstract: The maritime shipping industry is pivotal to global trade yet faces rising operational costs, particularly bunker fuel costs. This study develops an optimization framework that reformulates a real-world bunkering problem into a Mixed-Integer Quadratic Constraint Programming (MIQCP) formulation and solves it using the Gurobi Python API. Applied to an Asia-Africa liner shipping route, the model identifies an optimal bunkering strategy and selects 3 out of 14 ports. Furthermore, sensitivity analysis under varying fuel price scenarios provides robust insights into cost behavior.

Keywords: Design and reconfiguration of manufacturing systems, Enterprise modelling, integration and networking, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The complexity of industrial systems is growing due to increasing interconnectivity and digitalization in the era of Industry 4.0. To address this complexity, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) framework has been introduced and enhanced with Model-Based Systems Engineering (MBSE) through the RAMI Toolbox, offering structured methods for analyzing and designing interconnected systems. While RAMI 4.0 effectively supports the interplay of production systems, it falls short in addressing the unique challenges of flexible production systems, where the interactions between Product, Process, and Resource require more adaptable solutions. Additionally, as a generic framework applicable across various domains, RAMI 4.0 lacks the specificity needed to derive tailored models for particular industrial systems. This paper argues for the development of additional reference architectures to enhance the applicability and flexibility of RAMI 4.0. It proposes two novel reference architecture types designed to address the variability and complexity of flexible production systems. The effectiveness of these architectures is demonstrated through a case study involving a Palfinger use case, highlighting their potential to bridge gaps in current industrial practices.

Keywords: Production Control, Control Systems, Industry 4.0, Optimization and Control
Abstract: The design phase in injection molding is critical, as it directly influences prod- uct quality, production efficiency, and overall manufacturing costs. This paper introduces a multimodal machine learning framework to predict product quality during the design phase, whether for new products or modifications of existing ones. The framework integrates re- gression, classification, and probabilistic modeling to evaluate material characteristics and process parameters, providing dimensional and mechanical performance predictions for defined products. This methodology minimizes defect risks, optimizes process parameters, and refines production systems. The results highlight the framework’s potential to enhance efficiency, reduce waste, lower costs, and facilitate smarter, data-driven manufacturing aligned with Industry 4.0 principles.

Keywords: Enterprise modelling, integration and networking, Supply chains and networks, Optimization and Control
Abstract: This paper explores challenges and solutions for the interoperability between Swiss hospitals, rehabilitation centers, and insurance companies with a focus on the patient admission process. It addresses issues such as inconsistent data formats, little to no structured interfaces between institutions, and limited trust in a central authority. The study proposes two alternative architectures: a centralized system with encrypted data and a decentralized system using public key infrastructure (PKI). The findings suggest a hybrid model combining centralized and decentralized features as a feasible solution to enhance collaboration, optimize admissions, and improve patient care.

Keywords: Design and reconfiguration of manufacturing systems, Operations Research
Abstract: This study proposes a two-level optimization framework for heater placement in 2D systems like oven configurations, focusing on thermal uniformity and energy efficiency. The first stage minimizes the total absolute deviation of view factors from their average, ensuring uniform heat distribution across surface segments. The second stage minimizes the number of active heaters while maintaining the uniform heat distribution achieved in the first stage. By employing view factor-based heat transfer models and mathematical optimization, the framework provides an effective solution for designing energy-efficient and thermally uniform heating systems in industrial applications.

Keywords: Design and reconfiguration of manufacturing systems, Complex adaptive systems and emergent synthesis in manufacturing, Distributed systems and multi-agents technologies
Abstract: This paper introduces a conceptual framework for empathy-inspired self-organizing production systems to address the increasing complexity in manufacturing under VUCA and BANI conditions. By integrating the bio-inspired mechanism of empathy, the framework enhances adaptive capabilities of multi-agent systems, enabling agents to handle incomplete data, align local decisions with global objectives, and foster cooperative behavior. The 3E principle – empathetic perception, reasoning, and decision-making – is operationalized for enhancing agent properties. This approach aims to overcome current challenges in self-organizing production, providing a foundation for future innovations in resilient, efficient, and adaptive manufacturing systems while bridging the gap between theory and industrial application.

Keywords: Sustainable Manufacturing, Supply Chain Management
Abstract: Resource-efficient production is critical to addressing global resource scarcity and mitigating the environmental impacts of unsustainable business practices. Improving production efficiency helps to conserve materials, reduce waste, and improve sustainability within the scope of the circular economy. Adopting circular strategies is essential to address environmental challenges and achieve long-term sustainable development. The present paper proposes a preliminary research model for the assessment of the impact of circular economy measures on the resource efficiency of production and logistics systems in manufacturing enterprises. Following an examination of the fundamental principles of circular economy measures and resource-efficient production and logistics systems, the preliminary research model is hereby outlined and suggestions for the future are given.

Keywords: Knowledge management in production, Sustainable Manufacturing
Abstract: The transition to a Circular Economy (CE) in the industrial sector requires the identification and monitoring of effective Key Performance Indicators (KPIs), in order to measure the effectiveness of circular strategies in terms of resource efficiency, as well as environmental sustainability. However, today value chains are characterized by the presence and interactions of several actors, each one involved in different phases of the Product Lifecycle Management (PLM). Therefore, the identification and availability of reliable data to measure the selected KPIs can represent a challenge in this process, since data has to be retrieved from different source of the production chain, requiring effective information sharing approaches. This need can be addressed through the implementation of effective PLM approaches. To explore the potentiality of adopting PLM systems to support the monitoring of circularity and sustainability in the industrial sector, this study presents a bibliometric analysis aiming at highlighting the main trends currently characterizing this research topic. The results of the analysis show that literature on the topic is moving from a productivity-centered approach to a sustainability-driven one. However, further research is needed to provide more qualitative insights on the relevant literature.

Keywords: Smart manufacturing systems, Decision Support System, Sustainable Manufacturing
Abstract: The study presents a decision-making model for sustainable and circular manufacturing using the Analytic Hierarchy Process (AHP) and regression analysis. Key sustainability criteria, such as Circular Product Design, Eco-Friendly Materials, and Digital Integration, were prioritized through AHP. The model identifies Digital Integration and Closed-Loop Supply Chain practices as strong sustainability predictors. IoT-based real-time monitoring ensures adaptive decision-making in waste management, environmental conditions, and production. The model integrates blockchain for transparency and traceability, aligning with EU sustainability targets and offering actionable insights for manufacturers.

Keywords: Sustainable Manufacturing, Industry 4.0, Smart manufacturing systems
Abstract: Companies are increasingly concerned with the emissions produced by their activities. To assess and control these, a Life Cycle Assessment (LCA) is often performed, though it may require data which can be difficult and expensive to acquire. The present paper explores the utilization of existing technologies within manufacturing companies for this process. A literature review is presented, detailing the state of the art of this subject and presenting different tools being used today. Moreover, with the information gathered in this process, a framework is constructed where guidance is provided on digital technologies which can be used during the LCA, according to the practicality of the data acquired and the cost and effort required from the company. Furthermore, a case study in collaboration with a cosmetics manufacturer is elaborated in order to validate the proposed framework. The ensuing results demonstrate the applicability of the developed tool in real-life scenarios.

Keywords: Simulation technologies, Optimization and Control, Decision Support System
Abstract: This paper presents a high-fidelity simulation system for solar plant inspections using Unmanned Aerial Systems. It integrates Unreal Engine 5 for realistic visuals, AirSim for drone physics and sensors and Robot Operating System 2 for algorithm development. The system allows testing of inspection algorithms in a realistic virtual environment, improving the development of computer vision and object detection solutions. A case study using ArduPilot Flight Control Unit, Canny Edge Detection and PID control demonstrates its effectiveness.

Keywords: Robotics in manufacturing, Smart transportation, Distributed systems and multi-agents technologies
Abstract: This work researches the collaboration between unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to detect and track moving targets on the ground. The research includes three main components: a ground control station (GCS), a UAV, and a set of UGVs. The UAV provides a comprehensive view of the terrain for target detection. The UGVs are responsible for directly pursuing the target, receiving a set of routes. The GCS is responsible for processing aerial images and coordinating the UGVs. GCS uses deep learning to segment aerial images semantically, identifying ground structures inaccessible to UGVs. As a result, a navigability map of the terrain is produced. The GCS then uses this map to control the UGVs, estimating and transmitting possible routes to the desired target.

Keywords: Simulation technologies
Abstract: This paper presents the development and evaluation of the SAOA VANT simulator, a virtual reality-based tool designed to enhance artillery observer training through unmanned aerial vehicle (UAV) imagery. Building on a previously developed simulator for ground observation, this work introduces the aerial perspective as a natural progression for artillery training. The simulator was evaluated with 33 military personnel performing artillery observation tasks in a virtual environment. Participants completed multiple firing missions, followed by an assessment using effectiveness, presence, and sickness metrics. The results demonstrated the simulator’s effectiveness in improving observer accuracy and decision-making, with high levels of presence and minimal simulator sickness compared to established reference values. These findings highlight the simulator’s potential as a reliable and cost-efficient training tool, capable of enhancing operational readiness while mitigating common VR-related adverse effects.

Keywords: Smart transportation, Transportation Systems, Human-Automation Integration
Abstract: As urban airspaces become increasingly congested, managing autonomous UAV operations presents significant challenges, particularly in ensuring safety and efficiency through effective flight plan approval and conflict resolution mechanisms. This paper addresses these challenges by investigating the role of artificial intelligence (AI) in optimizing UAV operations within U-Space, focusing on strategic deconfliction and autonomous flight plan approvals. The primary aim of this study is to explore how AI techniques—specifically machine learning, optimization algorithms, and explainable AI (XAI)—can be integrated into UAV traffic management systems to support safe, scalable, and fair urban airspace use. The AI4HyDrop project is used to illustrate an AI-based approach in supporting fair and environmentally sustainable airspace use, addressing challenges in strategic deconfliction and airspace allocation. Ultimately, this work seeks to answer the question: "How can AI techniques be integrated to optimize autonomous drone flight plan approvals with mechanisms for strategic deconfliction in high-density airspaces?" By emphasizing the potential of AI to transform urban UAV operations, this study outlines key directions for research and collaboration in developing a safer and more efficient drone logistic operations.

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

Keywords: Supply Chain Management, Supply chains and networks, Decision-support for human operators
Abstract: A promising approach to advance the resilience of industrial value creation is the transition of manufacturing networks and systems towards Manufacturing-as-a-Service (MaaS). In this paradigm, manufacturing services are offered and consumed to create products. To realize MaaS, platforms are needed to bring service providers and consumers based on a matchmaking together. An approach to ensure the future-proofness of suchlike platforms and the related matchmaking is to use semantic technologies, which enable the extension of models, on which platforms and matchmaking are based, over time.

Keywords: Smart manufacturing systems, Industry 4.0, Sustainable Manufacturing
Abstract: As markets and supply chains become more volatile, rapid response to changing demands is crucial. Manufacturing as a Service (MaaS) enables quick access to manufacturing assets, while changeable manufacturing designs adaptable systems. This paper investigates how 1) manufacturing as a service may support changeability, and 2) how changeability may support manufacturing as a service. To do so, a representative literature review on the bidirectional support of changeability and manufacturing as a service is conducted. Consequently, the means changeability provides to enable and/or stimulate manufacturing as a service are studied. Additionally, how manufacturing as a service can support changeability in different levels of a production system is discussed. Findings show MaaS fosters manufacturing flexibility, while changeability strengthens MaaS in three areas: strategy (market adaptability), technology (flexible systems), and organization (operational agility). Together, they enhance manufacturing resilience and responsiveness.

Keywords: Production planning and scheduling, Industrial and applied mathematics for production, Operations Research
Abstract: Resiliency is the ability of a system to quickly recover from disruptions, which can lead to significant production perturbations and delays within manufacturing networks. This highlights the need to study resiliency not only at the strategic level but also at the tactical and operational levels, particularly in Make-To-Order (MTO) systems, which are inherently more susceptible to uncertainties and disruptions. As an operational level decision, Master Production Scheduling (MPS) undergoes potential disruptive events where constraints from suppliers, market demands, and production systems should be considered. In this context, Manufacturing-as-a-Service (MaaS) can be useful for mitigating supply chain risks. This research presents resilient MPS models for both proactive and reactive approaches aimed at minimizing production losses and recovery times in MTO systems, while also examining MaaS as an opportunity. By integrating empirical data with theoretical models, the results indicate that although the proactive approach incurs higher initial costs due to adjustments in the initial plan, it ultimately yields lower total costs compared to the reactive approach over the long term.

Keywords: Distributed systems and multi-agents technologies, Scheduling, Operations Research
Abstract: Recent advancements in digitization and shared economy business models have enhanced manufacturing competitiveness and resilience while bringing computational challenges in planning and scheduling service-oriented manufacturing systems where multiple customer orders (i.e., agents) are competing for shared resources. This study addresses this Multi-Agent Scheduling (MAS) problem, focusing on multi-facility with agents managing exclusive, non-overlapping orders. A novel mixed-integer programming (MIP) and Constraint Programming model are proposed, incorporating practical concerns; sequence-dependent setup times, facility unavailability and transportation time between facilities and order consolidation center. Experimental results demonstrate the effectiveness of both approaches in addressing challenges within multi-agent service oriented manufacturing systems.

Keywords: Pricing and outsourcing, Supply chains and networks, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Manufacturing as a Service (MaaS) represents a transformative shift in industrial production, offering flexible and scalable solutions through the use of shared manufacturing resources. However, the on-demand and variable nature of MaaS poses significant challenges in accurately estimating costs. This paper addresses these challenges by reviewing existing costing methods and selecting Activity-Based Costing (ABC) as the most suitable approach. A framework for cost estimation in this context is then proposed, followed by the use of simulation to support the implementation of ABC. The feasibility of this approach is demonstrated in a smart factory environment, showcasing how simulation can enhance cost estimation in a controlled setting. Finally, the methodology is examined from an industrial perspective, highlighting potential challenges and considerations for real-world application.

Keywords: Industry 4.0, Smart manufacturing systems, Design and reconfiguration of manufacturing systems
Abstract: This paper presents an integration methodology for Asset Administration Shell (AAS), AutomationML (AML), and OPC Unified Architecture (OPC UA) to address challenges in achieving seamless interoperability across engineering, runtime, and lifecycle data in Industry 4.0 ecosystems. The proposed approach combines AML for engineering data structuring, OPC UA for real-time communication, and AAS for unified lifecycle management, enabling seamless and efficient data exchange. The methodology was validated using a lab-scale production system, demonstrating its potential to streamline lifecycle management processes and enhance interoperability. While the integration was successful, challenges remain, highlighting the need for further standardization. This research provides a scalable framework for advancing digital transformation in manufacturing systems.

Keywords: Sustainable Manufacturing, Industry 4.0, Supply chains and networks
Abstract: The transition to a Circular Economy is critical for achieving sustainable manufacturing. However, challenges such as complex supply chains, lack of transparency and stakeholder collaboration are hindering progress. This paper examines the potential of Data Spaces to overcome these challenges and promote sustainable production practices. A systematic review of the literature and a bibliometric analysis were conducted to identify key research areas, geographical trends, synergies and gaps in the intersection of Data Spaces and Circular Economy. Subsequently, propositions were derived regarding the potential of Data Spaces to foster Circular Economy. By integrating Digital Product Passports and federated B2B marketplaces, Data Spaces facilitate recycling, repair and reuse processes, while simultaneously reducing the barrier to entry for small and medium-sized enterprises. The findings underscore the transformative role of Data Spaces in operationalizing circular principles. A research agenda is proposed to guide future empirical and theoretical studies in this domain.

Keywords: Design and reconfiguration of manufacturing systems, Smart manufacturing systems, Industry 4.0
Abstract: Consistent data integration along the life cycle of production systems remains a challenge due to multiple information modelling standards. AutomationML (AML) and OPC UA provide comprehensive frameworks for interoperability through mapping rules, configuration data integration, and industrial applications. An ongoing open-source development effort, hosted on GitHub (Automation ML e.V., 2024), aims to implement these mappings as XSLT transformations. This paper explores the practical application of reusing information and data models through AML–OPC UA mappings and evaluates three integration strategies: (1) transforming AML models into OPC UA information models, (2) embedding OPC UA server configurations within AML, and (3) integrating OPC UA information models into AML. A lab-scale proof of concept is conducted to validate these strategies, demonstrating their applicability and benefits. The results highlight how this integration can improve system flexibility, reduce engineering effort, and enhance data consistency across different life cycle phases.

Keywords: Production planning and scheduling, Industry 4.0, Production Control, Control Systems
Abstract: In today’s rapidly evolving manufacturing landscape, companies are facing increasing pressure to improve production efficiency, flexibility, and responsiveness. The integration of Industry 4.0 technologies immense potential to transform production planning and control (PPC) systems. Despite the growing interest in data-driven PPC, there is a clear gap in the literature: no paper has yet provided a comprehensive, focused overview of the challenges specifically faced by data-driven PPC systems in the context of Industry 4.0. This paper presents a comprehensive overview of the challenges that exist for data-driven PPC systems based on literature.

Keywords: Industry 4.0, Smart manufacturing systems, Design and reconfiguration of manufacturing systems
Abstract: The increasing demand for flexible and interoperable manufacturing systems necessitates the integration of digital technologies to enable real-time adaptability and data exchange. This research presents a methodology leveraging Asset Administration Shell (AAS) as a central enabler for smart manufacturing. By transitioning from static (Type 1) to reactive (Type 2) AAS and outlining a pathway to proactive (Type 3) AAS, key challenges in creating flexible systems are addressed. A system architecture integrating AAS with OPC Unified Architecture (OPC UA) and Programmable Logic Controllers (PLCs) is developed, enabling real-time synchronization. This modular framework aligns with Industry 4.0 principles, supporting scalable and adaptive manufacturing solutions.

Keywords: Human-Automation Integration, Decision Support System, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Small and medium-sized production companies face many challenges in decision making about planning, maintenance, and other operational processes due to challenges of high variety in customer demand, configurations of the production processes, technologies used and so on. Operation Digital Twinning is seen as a possible solution to cope with these challenges. However, the variety of influences inside and outside the production process create obstacles, hindering the design of a practical Digital Twin. The twinning system is identified as a socio-technical system where humans and machines work together. In multiple research projects, we identified three factors related to the design obstacles. These are related to 1) the predictability of the system, 2) the detectability of variations in process and environment, and 3) the usability of regulating the physical counterpart of the twin. We combined these three socio-technical factors of Predictability, Detectability and Usability to regulate in the PDU model. This model supports development of Operation Digital Twins to identify issues and steer system design.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Monitoring, diagnosis and maintenance of manufacturing systems, Simulation technologies
Abstract: In manufacturing, development approaches, model reuse and update process are important research subjects for data-driven simulation models and Digital Twins (DTs). As a result, an analysis of certain data-driven models and DTs is conducted to assess these topics. The main conclusions of the analysis are: first, the development approaches lack sufficient detail regarding the development of the simulation models. Second, the lack of detail and the unavailability of code make difficult the model reuse. And third, the information regarding the update process of the simulation model within a DT is either limited or absent.

Keywords: Industry 4.0, Decision Support System, Smart manufacturing systems
Abstract: In critical industrial applications, enhancing existing custom-designed products throughout their life cycles is crucial for improving user value while managing the costs of continuous design iterations. Additive manufacturing has demonstrated significant improvements in product performance through optimized designs, but implementing these improvements in scale requires excessive design efforts. In this paper, we address this gap by presenting a digital twin (DT)-based product design framework for additively manufactured parts that makes the design of the physical parts independently to adapt into their industrial application environments. The methodology is based on efficiently utilizing data in the intersection of application-specific DT model and the CAD (Computer-Aided Design). It is proposed that with the smart utilization of Evolutionary Algorithms (EA), most of the manual labor involved in drafting performance-improving revisions of design of part geometry could be eliminated. The proposition is evaluated from the business model point of view highlighting the potential for novel, mutually beneficial supplier-customer relationships in advanced industrial equipment solutions.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Industry 4.0, Decision Support System
Abstract: In this study, we focus on the role of digital twins (DTs) in industrial processes, concentrating on strategically important industries of automotive, aerospace and water treatment. In a context of increasing competition and rapid technological advances, DTs offer an innovative approach to the management of industrial operations. The study details how Internet of Things technologies (IoT), Artificial Intelligence (AI) algorithm and data analysis are integrated into DT systems. We also propose a detailed methodology for the implementation of DT combined with Statistical Process Control charts (SPC), which will be validated by a concrete application on a water filtration system. The results obtained thanks to this solution highlight the applicability of DT to optimise operational efficiency, anticipate anomalies and ensure real-time ( RT) monitoring and control using dynamic dashboards. This study answers the question of the importance of implementing a DT solution to achieve intelligent and sustainable production, while addressing major challenges such as data integration and RT synchronisation.

Keywords: Smart manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: Digital Twins have been used in industrial or manufacturing settings as digital replicas of physical objects, either material or machinery. This work proposes a platform that builds on information of lower-level digital twins to inform and contextualize upper-level digital twins. The proposed approach is demonstrated through a case study platform that aims to be able to track the work in progress status of manufacturing orders, and is a work in progress work which is being developed in the scope of a project in the textile and clothing (T&C) industrial sector. The goal is to demonstrate the feasibility of inferring upper-level digital twins from lower-level digital twins already in place in industries of the T&C sector.

Keywords: Human-Automation Integration, Robotics in manufacturing
Abstract: The rapid advancement of technology and global population aging are creating significant challenges for older workers in the modern workplace. This literature review identifies the psychosocial challenges faced by older employees when introduced to new technologies and digital tools. The increasing integration of artificial intelligence, automation, robotics, and digital tools presents both opportunities and challenges. While these advancements can enhance productivity and leverage older workers' experience, they also result in skill mismatches, adaptation difficulties, and preferences for early work exit. Older workers often face negative stereotypes, age bias, technology-induced anxiety, stress, challenges in managing digital distractions, physical limitations impacting technology use, and resistance to change. Despite growing research on technology's impact on the workforce, less attention has been paid to the specific psychosocial challenges older workers face. The selection of articles in English was made according to the inclusion criteria. The literature search encompassed various bibliographic-catalogue databases, including Web of Science, PubMed, Scopus, and Google Scholar. From the initial eight articles, we gained insight into the intersection of demographic trends, technological advancements, and the psychosocial challenges faced by older workers. This review addresses this critical gap by comprehensively examining the intersection of demographic trends, technological advancements, and the psychosocial challenges faced by older workers. The findings can inform strategies to support older workers' adaptation to technological changes, enhance job satisfaction, and promote continued workforce participation.

Keywords: Industry 4.0, Human-Automation Integration
Abstract: Industry 4.0 technologies have significantly transformed industrial environments and the way human work in operations systems. These developments also advance the Operator 4.0 and 5.0 paradigm, where technologies are utilized to either enhance workers' physical, cognitive, or sensory capabilities or to monitor their activities. Powered by wearable devices such as exoskeletons, smart glasses, and physiological tracking sensors, these systems aim to support workers in their tasks while providing insights into their performance, health, and well-being. A notable addition to this field is the concept of the Human Digital Twin (HDT), which seeks to create a virtual representation of a human worker. The HDT is envisioned to enable data-driven decision-making, optimize workflows, and enhance worker safety and well-being. However, these technologies pose significant ethical risks due to their dependence on extensive data collection, real-time monitoring, and integration into decision-making processes. Therefore, it is evident that these technologies introduce ethical and social risks, particularly concerning privacy, human autonomy, fairness, and human dignity. Although frameworks like GDPR exist to address some of these concerns, it remains unexplored how far the research on technological advancements in Operator 4.0, 5.0, and HDTs is keeping pace with the ethical considerations. To address this gap, we explored the research question: “What ethical issues are discussed, and to what extent, in the literature on Operator 4.0, 5.0 technologies and HDTs?.” We do this via a content analysis.

Keywords: Human-Automation Integration, Robotics in manufacturing
Abstract: As robotics continue to revolutionize production and intralogistics, there is a growing need to focus on human-centric approaches aligned with Industry 5.0 to ensure the well-being of workers. This paper presents an evaluation framework for robotic solutions in these settings, emphasizing the importance of human-robot interaction (HRI). By considering a human-centric approach, the study aims to guide organizations in evaluating human-robotic systems that prioritize well-being—including physical, cognitive, and psychosocial aspects— and complement rather than replacing human workers. The research categorizes robotic solutions based on their mobility and interactivity. While insightful, the framework has limitations, such as not fully capturing complex real-world scenarios and is non-exhaustive. The results highlight the positive impacts of HRI on human well-being while also addressing the potential challenges. The proposed evaluation framework provides a practical tool for decision-makers, helping them navigate the complexities of HRI towards human-centric industrial workplaces. This research contributes to the growing body of literature on human-centric robotics, offering valuable insights into the focus on human well-being in industrial settings.

Keywords: Human-Automation Integration, Industry 4.0, Smart manufacturing systems
Abstract: Wearable devices equipped with sensors are increasingly being used in workplaces to monitor worker health and safety, aligning with the human-centric focus of Industry 5.0. Enabled by the Internet of Things (IoT), these devices—embedded in garments, wristbands, or even applied directly on the skin—can measure a wide array of physiological functions. These include, but are not limited to, electromyography (EMG; muscle activity), electrocardiography (ECG; heart activity), electroencephalography (EEG; brain activity), blood pressure, skin temperature, and breathing rate. One use of the data collected is to monitor worker performance; however, making predictive and preventive decisions that ensure workers' health, safety, and well-being would also be possible. The use of such monitoring technologies in labor-intensive industries like intralogistics has not always been without controversy. For instance, in December 2023, the French Supervisory Authority fined Amazon France Logistique €32 million for violating General Data Protection Regulation (GDPR) rules through its employee monitoring practices in warehouses. Given this background, our scoping review seek answers to the research question (RQ): What insights does the literature provide on the impact of EMG and ECG sensors in enhancing decision-making and operational efficiency in real-world intralogistics work settings?

Keywords: Industry 4.0, Human-Automation Integration
Abstract: Digital transformation (DT), the strategic integration of digital technologies into all areas of an organization, fundamentally changes how it operates, delivers value to stakeholders, and adapts to evolving market demands. It is a critical driver for success in rapidly shifting, dynamic markets; however, literature indicates that up to 84% of DT initiatives fail to meet their objectives (Saldanha, 2019). Human factors (HF) such as mental, physical or psychosocial aspects have been found to play a deciding role in transformation success or failure (Neumann et al., 2021). Technology-focused strategies are therefore limited by employee demands calling for approaches that address the psychological and behavioral dimensions of change. Coaching with its roots in psychology, education, and management, emerges as a promising tool to navigate these complexities. Unlike traditional change management frameworks, coaching promotes individual and team growth, aligning personal goals with strategic objectives. In this article we provide a comprehensive understanding of coaching’s value in DT and examine how a novel combination of coaching interventions can be a solution to various DT challenges. First, we conduct a tertiary study to assess the state of knowledge on the effectiveness of coaching in a business context. Second, we perform a literature review highlighting current approaches for coaching in DT. Based on the results, we introduce the concept of Digital Transformation Coaching (DTC), examining its potential to resolve HF-related challenges to connect the issue of failing DT projects with the potential solution of coaching. Finally, we identify future research needs and managerial insights regarding DTC. We focused on the following research questions to elaborate the current state of and future potential for research. RQ1: What do literature reviews reveal about the overall effectiveness of coaching in organizational settings, and how might these findings inform its application in DT projects? RQ2: To what extent does the literature explore the role of coaching in overcoming resistance to change during DT? RQ3: How can current gaps in the literature be addressed to better understand the role of coaching?

Keywords: Smart manufacturing systems, Human-Automation Integration, Discrete event systems in manufacturing
Abstract: This study investigates the integration of Discrete Event Simulation (DES) and Digital Twin (DT) to enhance labor flexibility (LF) in Industry 5.0 settings. Industry 5.0 aims for a human-centered approach to manufacturing by combining advanced technologies with human adaptability, thus improving operational efficiency while emphasizing worker well-being. We propose a novel framework that enhances DES with real-time data, IoT sensor integration, and predictive analytics to facilitate the transition into a fully capable DT. This integration directly addresses the limitations of traditional LF models, which are typically based on static, pre-simulation analyses. By transforming DES into a dynamic and proactive DT, we achieve a system capable of real-time responsiveness, advanced labor allocation, and predictive adaptability, aligning closely with Industry 5.0 goals. Our findings demonstrate significant improvements in operational performance, including reduced idle times, optimized workforce utilization, and enhanced throughput, all while ensuring a human-centric approach that minimizes worker disruption. This study contributes to the evolution of labor management in modern production environments, bridging the gap between static simulation models and real-time adaptive systems.

Keywords: Production planning and scheduling, Knowledge management in production, Optimization and Control
Abstract: In today's evolving manufacturing landscape, where operational efficiency is crucial for maintaining competitiveness, accurate and adaptable cost allocation methods are vital. Time-Driven Activity-Based Costing (TDABC) has proven valuable for dynamic cost allocation, yet its integration with real-time data and predictive analytics within ERP systems like SAP remains underexplored, limiting its application in complex, variable processes. This study investigates how a TDABC-based model, incorporating real-time data and predictive analytics, enhances production scheduling, resource allocation, and operational efficiency in a manufacturing setting. Through a rigorous data collection and refinement process, including real-time validation and FMEA, the model improved cost accuracy, achieving prediction rates of 89% in the Hydrate department, respectively, while reducing discrepancies in automated processes. While the model significantly improved cost accuracy, ongoing variability in manual tasks highlights opportunities for further refinement and optimization. Findings support the potential of integrating TDABC with real-time data for data-driven AI solutions, advancing Industry 5.0 objectives for collaborative human-AI environments in manufacturing.

Keywords: Risk Management, Industry 4.0, Human-Automation Integration
Abstract: Motion analysis is essential in industrial settings for optimizing production, improving efficiency, and ensuring worker safety. Technologies such as Azure Kinect and OpenPose facilitate movement monitoring through image and video processing. This study compares two pose estimation methods by analysing joint angles – including the knee, elbow, neck, hip, and shoulder – during tasks performed by nine participants. Additionally, three applications involving different activities were evaluated to assess system reliability in workplace contexts. Results indicate that both systems detect angles accurately, with acceptable variation ranges across most tasks analysed. However, OpenPose showed low accuracy in capturing angles in the frontal view, highlighting a limitation of its 2D joint detection with frontal perspective and occlusions. For tasks involving frontal views or manual handling of large objects, we recommended cross-verifying OpenPose’s angle data, as Azure Kinect provides more consistent accuracy in these conditions.

Keywords: Human-Automation Integration, Smart manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Modern-day operations management is pivoting towards human factor issues. For instance, the Industry 5.0 concept emphasizes that human-centric approaches enhance resilience, sustainability, competitiveness, and well-being, necessitating new research methods. Supported by a practical business need from REWE, this paper investigates the application of such a human-centric approach by testing the possibility of assessing neurophysiological responses in three operational areas: assembly line management, order picking, and workload monitoring. We conduct three experimental pilot studies using wearable devices to demonstrate the potential of assessing real-time human responses potentially related to arousal, vigilance, ocular search strategies, and workload. These pilots have helped identify key methodological factors essential for human-centric research, such as ethical responsibility and compliance, technology acceptance, data management, and biosensor management. Building on these findings, the paper delves into methodological and theoretical advancements as well as practical business applications.

Keywords: Smart manufacturing systems, Decision-support for human operators, Human-Automation Integration
Abstract: Manufacturing enterprises increasingly integrate Artificial Intelligence (AI) into production processes to enhance efficiency and competitiveness. However, Responsible AI (RAI) practices are often overlooked, leading to potential ethical, legal, and operational risks such as biased decision-making and lack of transparency and trust. Existing maturity models, which evaluate the development stages of systems or practices, primarily address either Industrial AI (IAI) applications or general aspects of RAI. AI sandboxes, as controlled environments for testing and validating AI systems under regulatory supervision, offer a structured approach to assess RAI practices. Yet the frameworks lack a targeted model for determining RAI maturity specifically within manufacturing. To address the gap, this paper introduces a RAI-oriented maturity model adopted to the context of smart manufacturing. The maturity model evaluates AI systems across six key dimensions: Accountability, Fairness, Human-Centricity, Privacy/Security, Sustainability, and Explainability. In particular, this paper delves into the dimension of accountability, exploring its indicators. A structured literature review defines the indicators, and a pairwise comparison determines their relative importance. The model is applied to an industrial case study involving a Large Language Model (LLM)-based chatbot for maintenance in the semiconductor industry. Preliminary results demonstrate the model’s practicality and effectiveness, providing actionable insights, i.e. the need for auditing mechanisms, to enhance accountable AI design and deployment in industrial settings.

Keywords: Production planning and scheduling, Industrial and applied mathematics for production, Industry 4.0
Abstract: This study addresses a dual resource constrained flexible job shop scheduling problem occurring in a textile sewing shop with multi-purpose machines and multi-skilled operators. Sequence-dependent setup times may occur for both operators and machines. Two objectives are considered, namely the total tardiness and the resource occupation. A heuristic exploiting the earliest starting time and the estimated tardiness of jobs is proposed to address this problem. A mixed-integer linear programming model aiming at minimizing the total tardiness is used as a benchmark. A detailed computational analysis is carried out on a set of industrial instances. The performance of the heuristic is evaluated on both criteria. Results highlight the relevance of the heuristic in obtaining solutions that balance total tardiness and resource utilization, demonstrating its applicability to real-world scenarios.

Keywords: Smart manufacturing systems, Scheduling, Sustainable Manufacturing
Abstract: Sustainability and digitalization fundamentally change how production is planned and controlled. One of the central parts of production control is job scheduling, which has the potential to include vast amounts of data to improve the quality of solutions. However, solving the scheduling problem is not limited to financial or operational aspects anymore; solutions must also include social and environmental aspects. While this issue is increasingly recognized among a plethora of other aspects in literature, many approaches still only consider small subproblems leading to a strong fragmentation. This study, therefore, develops a holistic framework for intelligent scheduling to integrate sustainability and social responsibility objectives while simultaneously offering a solution to bringing together the multitude of influencing factors in the domain of scheduling. The development of the concept is based on a structured literature review and an expert questionnaire. The Scalable Modular Architecture for Scheduling is a module-based software solution that connects to the real production system based on asset administration shells and graph-based problem modeling. The graph is transformed into a vectorized representation using a heterogenous graph neural network and combined with energy and maintenance-related information to be fed to a scheduling agent. This module utilizes a proximal policy optimization architecture to select the next best action based on mutliple criteria regarding economical and sustainable aspects and feed this information back to the real system, closing the loop. The framework is validated on benchmark data sets.

Keywords: Production planning and scheduling, Operations Research
Abstract: We combine a multi-period optimization model for order release planning (allocated clearing function model) with a short-term order release mechanism (LUMS). Both methods complement each other since the explanatory models of the material flow are consistent and their combination performs mid-term release planning and short-term release control which are both needed. Numerical experiments show that this novel approach substantially improves due date performance and cycle times at the cost of a moderate increase in FGI compared to a stand-alone allocated clearing function model.

Keywords: Inventory control, production planning and scheduling, Supply chains and networks, Operations Research
Abstract: In the automotive industry, production planners adhere to the promised release dates, which are calculated based on the components’ availability and the client delivery lead times. Recently, integration efforts led to considering the inbound and outbound constraints to build a holistic production plan. However, these integration approaches need to consider the financial performance of the supply chain. This paper demonstrates how respecting release dates considering the invoicing lead times, which is the time elapsed between the release of the manufacturing order from the factory until invoicing the order, could significantly improve the invoice rate, directly contributing to the cash flow performance. We present our work on a real industrial use case from a car manufacturing site in Spain. Our results show that the number of manufacturing orders risked late invoicing is reduced by 76%.

Keywords: Production planning and scheduling, Decision Support System, Optimisation Methods and Simulation Tools
Abstract: This work explores a novel resource allocation problem where a limited resource, such as machine time or budget, is distributed among multiple agents over discrete time slots. Each agent has indivisible unit demands and preferences for the order in which their units are served. The study aims to find fair solutions that balance agents' individual preferences and overall efficiency. To tackle this challenge, a Mixed-Integer Linear Programming (MILP) model is proposed to account for both demand allocation and order preferences. Computational experiments assess the model’s effectiveness and evaluate the trade-off between fairness and efficiency. Results indicate that in small instances, fair solutions remain close to the system optimum with minimal efficiency loss. However, as complexity increases, maintaining fairness becomes significantly more costly.

Keywords: Transportation Systems, Smart transportation, Industry 4.0
Abstract: Reducing CO2 emissions from transport is one of the climate change targets. Car sharing is becoming increasingly popular in many cities. Taking these two facts into account, the authors conducted a study using a specially designed sensor to collect data from the car and an analytical system to identify the driver's driving style during car rental. The study analysed more than 770,000 journeys made by 2,061 internal combustion engine cars fitted with the developed sensor. The results were divided into two groups: drivers who did not use the mobile application (45 650 drivers) and therefore did not receive an assessment of their driving style and tips on how to improve it, and drivers (4 453) who agreed to use the application and follow the guidelines it suggested. The study showed that by using a solution that identifies and provides information about a driver's driving style, it is possible to change that style to one that results in lower fuel consumption and therefore lower CO2 emissions. Analysis of the results showed that the CO2 emissions of drivers who followed the eco-driving guidelines were 14.4% lower than those of drivers who did not. This is a statistically significant difference.

Keywords: Design and reconfiguration of manufacturing systems, Industry 4.0, Distributed systems and multi-agents technologies
Abstract: Scaling up energy renovations in construction requires industrialization to address economic, environmental, and social challenges. Movable factories, such as Modern Flying Factories (MFF) and Factory-in-a-Box (FiaB), offer innovative solutions for massification. This study combines insights from a systematic literature review (SLR) with a novel analytical framework, developed through expert workshops, to evaluate these concepts across lifecycle stages and performance dimensions. This research provides actionable insights to align movable factories with sustainability and industrial scalability goals.

Keywords: Modelling Supply Chain Dynamics, Decision Support System, Supply Chain Management
Abstract: Quick Commerce (Q-commerce) has transformed the retail landscape by offering superfast delivery services to customers within 10-15 minutes of ordering. However, the rapid expansion of Q-commerce retailing has significantly outpaced the growth of traditional retail. The growth of the Q-commerce retail market drives numerous hyperlocal retailers and e-commerce giants to establish their own Q-commerce applications. However, online grocery penetration remains relatively low at 1-2 percent; this is because the Q-commerce companies mainly operate in Tier-I cities. Q-commerce companies face logistical challenges while expanding to suburbs (Tier-II & Tier-III cities), like setting up and expanding dark stores (micro-fulfilment centers), intense competition from local retail stores, and regulatory compliances. Whereas hyperlocal retailers or local farmer markets are important to neighborhoods because they offer special services and unique items to customers that are not usually available on Q-commerce platforms. Therefore, it will be a win-win situation for Q-commerce companies and hyperlocal retailers to collaborate with each other. In the paper, we propose a new methodology to foster trust-based interactions between Q-commerce companies and hyperlocal retailers by introducing the concept of Limited Trust Equilibrium (LTE). We analyze the existence and properties of LTE in the context of Q-commerce coordination by assessing the individual utility of the Q-commerce company and the hyperlocal retailer and the overall net utility Lastly, this study also discusses the various managerial implications for both the Q-commerce company as well as the hyperlocal retailer.

Keywords: Sustainable Manufacturing, Supply Chain Management, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Carbon capture, utilization, and storage (CCUS) is a critical decarbonization technology for the oil and gas (O&G) industry. By implementing CCUS, carbon emissions can be captured, stored, and later reused for enhanced oil recovery (EOR). However, investing in CCUS presents challenges, primarily due to the mismatch between carbon capture capacity and demand. This paper introduces a two-period model for an O&G system that includes one O&G manufacturer and one CCUS provider. The manufacturer generates products by incurring production costs while producing carbon emissions. We analyze how CCUS capacity affects optimal operational decisions and investment strategies in this system. Our findings reveal that CCUS enables net-zero emissions and enhances profitability for both entities within the O&G system, even when high-emission products are produced. However, these benefits diminish when production costs are low. Furthermore, the results highlight optimal CCUS strategies under varying product cost conditions: For low-cost products (e.g., asphalt, petroleum coke), partnering with a CCUS provider with higher capacity improves profits and social welfare. Conversely, for high-cost products (e.g., gasoline), collaboration with CCUS providers enhances the manufacturer’s profitability through a high EOR rate but does not improve social welfare. Overall, this study provides managerial insights into designing an optimal CCUS investment strategy that balances environmental and economic objectives.

Keywords: Sustainable Manufacturing, Distributed systems and multi-agents technologies, Smart manufacturing systems
Abstract: This paper presents a novel approach to integrated manufacturing-microgrid control using multi-agent deep reinforcement learning. We address the fundamental challenges of coordinating production schedules with variable renewable energy sources through a distributed control framework based on Partially Observable Markov Decision Process (POMDP). Our framework combines state-of-the-art reinforcement learning algorithms with domain-specific constraints to handle uncertainties in both manufacturing operations and renewable energy generation. Experimental results demonstrate that our Soft Actor-Critic (SAC) implementation achieves superior reward performance compared to Proximal Policy Optimization (PPO), while PPO shows better stability in production rates. The algorithms exhibit distinct battery management strategies, with PPO utilizing a wider State of Charge (SOC) range for maximum capacity utilization and SAC maintaining a more conservative range for enhanced stability. These results demonstrate the viability of our approach for sustainable manufacturing systems requiring both high performance and operational stability.

Keywords: Robotics in manufacturing, Optimization and Control, Production Control, Control Systems
Abstract: In many manufacturing systems, precise movement along a given path of a mobile robot is an important control task. To achieve effective motion control, especially at relatively high speeds, the dynamics of the robot must be taken into account. (instead of seeing the system as a point in the workspace or presenting it using only kinematic equations). Thus, this paper addresses the point-to-point control problem for a differentially driven nonholonomic Wheeled Mobile Robot (WMR) with nonlinear dynamics equations which may be applied in a relevant production environment. Based on the flatness property we present a motion planning procedure which determines a path from initial to final oriented points, i.e. points with associated prescribed initial and terminal directions of the WMR in a 2D workspace. In particular, using previous results we present here a suboptimal control law that ensures the transfer of the vehicle from a given initial state to a defined final state while minimizing a selected quadratic index of performance in following a reference path connecting the initial and final oriented points. Several examples illustrate the uses of the control algorithms with possible applications in a working environment within manufacturing places are presented throughout the article.

Keywords: Scheduling, Optimisation Methods and Simulation Tools, Robotics in manufacturing
Abstract: The scheduling of pick-and-place tasks in systems with multiple robots in shared workspaces is a well-studied topic with significant industrial relevance. We address a complex scenario involving four heterogeneous robot arms operating within a limited shared workspace, which was introduced by El Ghazi et al. (2024). Their approach leverages mixed-integer linear programming (MILP) modeling using trajectory simulations, achieving low makespans but at the cost of high computational time and model complexity. To improve efficiency, we propose an alternative approximation method based only on the durations of the trajectories and the relative positions of robots and objects, without prior knowledge of the space taken by the robots in their trajectory, therefore bypassing the need for spatial trajectory simulation. Our model incorporates hitboxes to approximate the space occupied by robots during tasks, therefore predicting collisions between robots, while reducing computational load. Experimental comparisons indicate that our method is significantly faster than the other approach. For larger instances (ten objects to pick up), our model often outperforms El Ghazi et al.’s model in terms of makespan.

Keywords: Robotics in manufacturing, Line Design and Balancing, Operations Research
Abstract: The multi-model multi-robotic disassembly line balancing problem (MM-MRDLBP) aims to optimize disassembly operations in sustainable manufacturing. This study tackles this problem by minimizing the number of robots required to implement the disassembly operations in the disassembly area, where several robots in the disassembly line disassemble the end-of-life (EOL) products. A mathematical model is utilized as an optimization tool to minimize the number of required robots during different periods. The EOL products are assigned to be disassembled in the line in a multi-model, where different EOL products could be assigned to be disassembled on the line simultaneously. Optimizing the number of robots reduces the system's total cost. It also leads to the creation of a flexible smart system. The findings help the decision-makers to determine the best combination of EOL products that the minimal number of robots could disassemble during different periods.

Keywords: Smart manufacturing systems, Optimization and Control, Industrial and applied mathematics for production
Abstract: Robotic arms are extensively used in production environments to undertake tasks such as welding, hemming, etc. Minimizing energy consumption of robotic systems poses a critical challenge for sustainable manufacturing. We propose using the robot's Digital Twin to obtain the energy consumption for each movement of a production cycle under different operational scenarios, i.e., different configurations for attributes such as velocity, acceleration, jerk and trajectory. Further, we develop an Integer Programming (IP) model that incorporates these scenarios and selects the ones that minimize total energy consumption. To facilitate applicability, we present a preprocessing filter that uses Pareto dominance to remove suboptimal scenarios, reducing the IP's solution space and thus vastly improving computational efficiency, as also shown in our computational experiments. Moreover, we present how we seamlessly apply our approach within the design process of robotic cells.

Keywords: Robotics in manufacturing, Industrial and applied mathematics for production, Optimisation Methods and Simulation Tools
Abstract: This paper addresses the challenge of optimizing pick-and-place robotic operations using a Kitting Robot System in the pre-assembly stage of car manufacturing. The objective is to minimize the time required to transfer car components from containers to kit-holders for subsequent assembly. We model this problem as a Bipartite Traveling Salesman Problem (BTSP) and apply various solution approaches. Specifically, we utilize two well-known heuristic methods, namely Nearest Neighbor (NN) and the 2-opt approximation algorithm, as well as an Integer Programing model adapted for BTSP. Moreover, we explore the applicability of a Reinforcement Learning approach based on Q-learning. By analyzing computational times and solution quality, we provide insights into the efficiency of these methods in an industrial setting, highlighting their suitability based on problem size and complexity.

Keywords: Smart manufacturing systems, Decision-support for human operators
Abstract: In recent years, generative artificial intelligence (GAI) has gained significant importance in production and operations management (POM) due to its potential to enhance worker productivity. This article aims to characterise the impact of GAI on workload, efficiency and labour productivity across various industries. The research question was formulated and, using the CIMO framework (context, intervention, mechanism, outcome), the search and retrieval of articles were conducted in the Scopus and Web of Science (WoS) databases, and yielded 149 articles. After the selection, evaluation and content analysis of each study, 74 articles were ultimately included in the systematic literature review. Seven industries were identified in which GAI has demonstrated impacts on workload, efficiency and labour productivity, with four sectors accounting for 80% of the studies. The impacts of GAI reveal four trends, all of them key in POM: automation and optimisation of workflows; support in decision making; improvement in human-machine interactions; enhancement in communication. To fully apply the potential of this technology, it is necessary to continue researching and addressing the identified issues, including ethical, employment, privacy and information quality challenges.

Keywords: Quality management, Decision-support for human operators, Industry 4.0
Abstract: The convergence of Industry 4.0 technologies, human-centric approaches, and quality management is shaping the emerging paradigm of Quality 5.0. This study develops a conceptual framework for Quality 5.0, illustrating the potential pathways industries can follow to effectively transition to this paradigm. Specifically, the framework identifies three distinct starting points: Human-Centric Industry 5.0 (HC I5.0), Quality 4.0, and Human-Centric Quality (HC Quality). Industries occupy distinct positions in this framework based on automation and digitalization levels. Identifying their starting point helps determine the strategic assets for a successful transition. Drawing on a comprehensive literature review and relevant case studies, this study examines the framework within the context of the fashion industry, to assess its positioning and examine the most covered areas among the intersections. The results reveal a focus on HC Quality approaches, with limited practical implementation of Quality 4.0, particularly in the luxury segment. The paper highlights the importance of leveraging existing frameworks for a strategic progression towards Quality 5.0, offering theoretical and managerial perspectives alongside practical insights for fashion companies navigating the shift toward a more sustainable, efficient, and HC production environment.

Keywords: Sustainable Manufacturing, Industry 4.0, Decision-support for human operators
Abstract: The advent of the Industry 5.0 paradigm, a socio-technical production concept that develops the automation principles typical of the Industry 4.0 approach according to a more “human-value centered” conception, has prompted a rethink of manufacturing organizations, especially in terms of sustainability. In accordance with the tenets of the Industry 5.0 paradigm, a company can be defined as effectively sustainable if it encompasses three main factors: environmental sustainability, social sustainability, and intrinsic supply chain resilience. In this context, several approaches have been put forth in scientific literature as potential means of pursuing the sustainability goals that companies are required to meet. Among these, the production methodology proper to Lean Management presents a set of production principles aimed at maximizing value for the customer while placing the human element at the center of the production system, therefore constituting the very premises of the Industry 5.0 paradigm. Despite the existence of several studies conducted with the aim of understanding the extent to which this methodology may be applicable in the context of manufacturing sustainability, they have not yet succeeded in developing a comprehensive and structured theory related to this topic. In light of the aforementioned considerations, the objective of this paper is to present a more comprehensive account of the existing literature on the subject of the applicability of the Lean paradigm to the principles of Industry 5.0. To this end, a bibliometric analysis was performed for its capability of studying the progress of scientific research on a specific topic. The results provided insights into the current state of knowledge in the field and identified areas where research is lacking, especially the ones pertaining to the potential role of lean manufacturing within the paradigm of 'human-centered manufacturing' from a learning/training perspective, and supply chain resilience. The proposed research agenda highlights how Lean principles will play a significant role in supporting industrial change in the near future, aligning with the concepts central to the I5.0 paradigm.

Keywords: Human-Automation Integration, Enterprise modelling, integration and networking, Scheduling
Abstract: The paper reviews the ongoing issue of interoperability at an organizational level and its importance in the context of Industry 5.0, focusing on the building and maritime industries. The paper explores the potential for cross-industry learning between the two industries, highlighting data domains that can improve project flow while analyzing lessons learned and challenges in the adoption of modern tools for data sharing and management. Findings suggest that establishing clear legal frameworks, adaptable work processes, and cultural dynamics are necessary for improving interoperability. Furthermore, the paper supports a holistic approach that considers human, organizational, and legal dimensions, highlighting guidelines from the European Interoperability Framework (EIF) to improve collaboration across these industries.

Keywords: Decision-support for human operators, Smart manufacturing systems, Design and reconfiguration of manufacturing systems
Abstract: This paper investigates the integration of the Lean manufacturing and Human-Centric (HC) concepts in the fashion industry through a multiple case study using a custom-developed assessment model. Ten companies were evaluated across three dimensions—Awareness, Implementation, and Effectiveness—in both Lean and HC domains. Our findings reveal varying maturity levels between companies, with some standing out in a positive way ahead of the rest due to dedicated Lean teams and supportive corporate cultures. However, other companies struggle due to e.g. limited resources and high resistance to change. The proposed assessment model provides a practical tool for measuring current practices, and highlight how organizational structure, targeted strategies, capabilities and practices can enhance Lean-HC.

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

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

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

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

Keywords: Human-Automation Integration, Quality management, Design and reconfiguration of manufacturing systems
Abstract: This study empirically investigates how Explainable Artificial Intelligence (XAI) affects defect detection and balanced accuracy in visual inspection tasks. Using a laboratory experiment, 34 participants, randomly assigned to either AI or XAI conditions, completed 21 visual inspections each. Our results show XAI significantly improves both defect detection and balanced accuracy, supporting the potential of XAI in manufacturing quality control. Further, we explore task-related experience as a moderator for the relationship of AI/XAI and defect detection rate. This study advances our understanding on human-AI collaboration in quality control, highlighting the value of transparency and trust through saliency maps in AI-assisted systems.

Copyright © 2025 IFAC

Keywords: Decision-support for human operators, Optimisation Methods and Simulation Tools, Simulation technologies
Abstract: This study proposes a virtual agent using agent-based modeling to simulate operators' behavior and sensitivity to their environment. The goal was to develop and calibrate a human-like agent that can be used for future work in distribution center environments using agent-based simulation. By developing a human-like agent, productivity and accuracy were analyzed by considering various environmental control variables. The methodology involved selecting relevant factors, researching mathematical models, collecting data, calibrating and validating the models, and developing a simulation model. The results of the simulation model analyzed the impact of changes in schedules, experience level, task intensity, rest quality, and layout on operator productivity and accuracy. The findings revealed that fatigue and experience significantly influence operators, with factors such as task intensity, rest quality, layout, and schedules affecting their performance. The results used data collected from a real distribution center operation of a Portuguese retailer.

Keywords: Production planning and scheduling, Inventory control, production planning and scheduling, Decision-support for human operators
Abstract: As the proportion of older workers increases, considering their unique characteristics and needs in operations management has become critical, especially in manufacturing environments. Traditional production scheduling models usually assume a homogeneous workforce and neglect the diverse physical and cognitive abilities of older workers. This paper presents a systematic literature review to evaluate the state of research on age-inclusive production scheduling models. The results indicate that besides job rotation scheduling problems, production scheduling that considers the sequence of jobs at machines or workstations in connection with the age-inclusive assignment of workers has received little attention. To close this gap, we propose a dual resource-constraint job shop scheduling approach incorporating workers’ experience and age based on the results of the literature review and discuss future research opportunities.

Keywords: Decision Support System, Scheduling
Abstract: Order picking is a labor-intensive process that imposes varying physical workload on workers. To address workforce heterogeneity and reduce health risks for order pickers, an ergonomic order assignment model (EOA) is developed to balance workload distribution among employees. This paper specifies the features of the model aiming at the reduction of physical workload and the achievement of its applicability in industry for picker-to-parts processes. Based on the framework of socio-technical systems with the subsystems human, organization, and technology, design criteria are derived to concretize the EOA. Requirements and limitations are discussed based on literature and industry findings, structured by the interfaces of the subsystems.

Keywords: Smart manufacturing systems, Design and reconfiguration of manufacturing systems, Human-Automation Integration
Abstract: Modern industrial assembly poses high productivity demands, which has led to the adoption of instructional systems. Although instructional systems such as augmented reality or screen-based systems have been shown to reduce mental effort and errors, they are typically evaluated against paper-based instructions, and the effects of instructional modality remain under-researched. This paper investigated three different instructional modalities, including text, audio, and a virtual avatar. We performed a withing-subject study with 36 participants and tested for the effect of instruction provision, and the preferences for a particular modality, on the efficiency and subjective experience in assembly systems. While the effect of instructional modalities wasn't significant, our findings show a significant interaction effect between the modality and the user preference. Notably, descriptive statistics indicate that audio instructions resulted in the fastest task completion time in users that prefer audio modality. These results underscore the potential of multimedia-instructional systems for industrial environments, demonstrating that offering multimodal interfaces could improve productivity.

Keywords: Supply Chain Management, Supply chains and networks, Decision Support System
Abstract: Generative Artificial Intelligence (GAI) revolutionizes supply chain management (SCM) by facilitating resource orchestration via predictive analytics, intelligent resource allocation, network synchronization, and ongoing learning. This research relies on Resource Orchestration Theory (ROT) to examine the role of GAI in organizing, integrating, and utilizing resources within SCM. A systematic literature review (SLR) was performed, incorporating findings from 32 research papers to propose a conceptual framework that aligns GAI-enabled capabilities with resource orchestration processes, specifically elucidating how GAI facilitates the structuring, bundling, and leveraging of resources. Then, this proposed paradigm demonstrates the relationship between GAI's predictive and adaptive capabilities and the essential processes of resource orchestration, offering a systematic method for comprehending its contribution to improving supply chain operations. The results highlight GAI-enabled capacities to enhance decision-making, optimize resource distribution, and bolster supply chain resilience and efficiency. The study improves theoretical understanding by extending the relevance of ROT to digital supply chains and provides practical insights for managers seeking to integrate GAI into supply chain operations. This study identifies GAI as a crucial enabler of competitive advantage in dynamic supply chain environments by integrating technical capabilities with resource management tactics.

Keywords: Supply Chain Management, Decision Support System
Abstract: Integrating generative artificial intelligence (Gen-AI) into humanitarian operations presents a transformative opportunity to enhance decision-making and resource allocation. This paper explores how Gen-AI can address data management challenges in humanitarian contexts, thereby supporting decision-making and resource allocation in humanitarian operations. We examine the practical benefits and challenges of implementing this technology by studying three pilot projects: the Humanitarian Data Insights Project, the UNHCR and Arm partnership, and the WFP’s voice-to-text project. The findings highlight the potential of Gen-AI to streamline data processes, enhance accuracy, and facilitate real-time insights while also addressing concerns related to bias, data privacy, and accountability. The paper concludes with recommendations for humanitarian organizations to effectively leverage Gen-AI, emphasizing the importance of relevant data governance, AI literacy, and cross-sector collaboration. This study contributes to the growing body of knowledge on applying advanced technologies in humanitarian operations, offering insights for future research and practical implementation.

Keywords: Industry 4.0
Abstract: Customer expectations are no longer confined to product quality or price offerings. Modern firms now emphasize the overall customer experience associated with acquiring a product or service, recognizing its significance in shaping customer satisfaction and loyalty. Since the technological scenario is changing rapidly, with generative AI (GAI) enhancing the capabilities of AI, the proposed research intends to examine the future transformational role and capabilities of GAI in customer experience management (CEM) for effective product management. A comprehensive analysis of AI and GAI’s capabilities was conducted to identify the impact areas of traditional AI that are enhanced by GAI. The study further mapped the functional enhancements offered by GAI across the core elements of CEM identified in the literature. further. The potentiality of GAI assisted CEM was also discussed within the context of effective product management.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Monitoring, diagnosis and maintenance of manufacturing systems, Smart manufacturing systems
Abstract: The manufacturing industry is increasingly adopting a computational approach that relies heavily on process data for operational insight. Anomaly detection plays a crucial role in providing a thorough understanding of process behavior, helping operators determine if their production systems are operating optimally or if proactive intervention is required. A significant challenge with machine learning-based solutions is their lack of interpretability, making it difficult to understand the reasoning behind model predictions. This paper addresses the need for interpretability in anomaly detection using Transformer networks, achieving 82% accuracy in the experiments conducted for this study, where the minority class required half the data augmentation applied to the majority class for balance. The explainable AI framework known as Local Interpretable Model-agnostic Explanations (LIME) is used to elucidate the critical features and their interactions that influence individual predictions. Traditionally used to interpret sequential neural network models, this study extends the application of LIME to Transformer models for anomaly detection in manufacturing processes. This method not only enables anomaly detection but also helps to identify key features that signal anomalies, thereby improving process management and control.

Keywords: Transportation Systems, Optimization and Control

Keywords: Supply Chain Management
Abstract: The demand for rare earth elements (REEs) has grown significantly due to their critical role in advanced technologies, including renewable energy systems, electric vehicles, and electronics. However, the European Union (EU) relies entirely on imports to meet its REE needs, exposing industries to significant supply chain vulnerabilities. The paper provides a comprehensive analysis of the current state and future potential of the REEs recycling value chain in Europe, identifying enabling factors and barriers across the upstream, midstream, and downstream stages. Based on findings developed in three EU and national funded research, this paper highlights the huge potential of available recycling technologies but also criticalities in terms of waste collection systems, recycling infrastructure, and downstream processing capabilities. The paper offers some implications for policy makers at national and EU levels.

Keywords: Supply Chain Management, Supply chains and networks, Decision Support System
Abstract: Integrating Digital Twins (DTs) within supply chain management offers transformative potential for optimizing operations, enhancing decision-making, and fostering resilience. However, existing literature often lacks practical insights into assessing their maturity. This paper addresses these gaps by proposing a comprehensive Maturity Model (MM) tailored for supply chain DT development. The proposed MM is applied to real-world case studies, highlighting its utility in evaluating DT readiness and guiding implementation. Key challenges, including modeling capabilities, data and system integration, and stakeholders’ collaboration, are discussed alongside strategies for overcoming them. This research provides practitioners with actionable insights for building robust DT architectures, enabling organizations to leverage the full potential of digital transformation while ensuring scalability and sustainability.

Keywords: Supply chains and networks, Modelling Supply Chain Dynamics, Complex adaptive systems and emergent synthesis in manufacturing
Abstract: Circular Economy Networks (CENs) are sustainable ecosystems where multiple stakeholders synergically operate to maximize the re-circulation of end-of-life resources. The collaborations established among them produce complex and dynamic structural archetypes, combining chain-like structures with a new set of short and long loop-like structures, serving for the recovery of secondary resources. Scholars have also noted that CENs evolve over time, influenced by the collaborative behaviors of the involved stakeholders. However, the effectiveness and efficiency of collaborative mechanisms both require further attention. We address this topic by employing Agent-Based Modelling and Simulation methodology. The results of simulations, identifying the most effective and the most efficient mechanisms on complex network patterns, provide both theoretical and managerial contributions.

Keywords: Robotics in manufacturing, Monitoring, diagnosis and maintenance of manufacturing systems, Optimization and Control
Abstract: This study introduces the Direct Step Edge Follower (DSEF), a novel algorithm for detecting and following edges in images. DSEF leverages stepwise directional refinement and kernel-based statistical testing to achieve high precision while minimizing computational costs. It was evaluated in solar panel inspection with Unmanned Aerial Vehicles and compared against the Canny edge detection algorithm. DSEF surpasses Canny in identifying relevant edges, particularly in complex lighting conditions, with short processing time, achieving a lower mean error (-0.152m vs. -0.165m) and more accurate angle detection (-0.16 deg vs -0.20 deg error) compared to Canny.

Keywords: Transportation Systems, Smart transportation, Decision-support for human operators
Abstract: This paper proposes a vehicle detection system for roundabouts based on images captured by a drone. This system runs on a Big Data architecture to ensure scalability and real-time processing. The system architecture is divided into two parts: a detection part, based on drones and computer vision, and a communication and processing part, based on a Big Data architecture deployed in the cloud. The system is able to accurately detect both roundabouts and the vehicles driving on them, providing valuable information on traffic conditions. The Big Data architecture allows real-time traffic information to be processed and analyzed, facilitating informed decision-making to improve traffic flow and safety. The evaluation of the system, carried out through simulations, has demonstrated its robustness and ability to handle large volumes of data in real time.

Keywords: Simulation technologies, Smart transportation, Distributed systems and multi-agents technologies
Abstract: This paper presents a comparative analysis of two reinforcement learning algorithms, Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), applied to the task of UAV obstacle avoidance. The work evaluates these models in a 3D simulation environment using Unreal Engine and AirSim API, analyzing their performance to highlight the potential and challenges of reinforcement learning in UAV control tasks. The results demonstrate that DQN achieved substantially superior performance across mission success and collision efficiency metrics compared to PPO. This performance gap arises from DQN’s off-policy design and discrete action-space compatibility. In contrast, PPO’s policy gradient framework—optimized for continuous control—faced significant challenges in discretized environments, leading to unstable policy updates and inefficient exploration. These findings underscore the critical role of action-space alignment in RL algorithm selection.

Keywords: Smart transportation, Industry 4.0, Transportation Systems
Abstract: This paper presents a systematic literature review of the application of Drones, or Unmanned Aerial Vehicles (UAVs), in the healthcare sector with a specific focus on their applications in remote and rural areas. While urban areas encounter healthcare service delays due to congestion and logistics inefficiencies, rural regions struggle with geographical isolation and lack of proper infrastructure. The results of the literature analysis are presented considering the application of drones in different stages of a healthcare value chain, i.e., prevention, diagnosis, treatment, and recovery stages. The paper further explores the challenges discussed in the literature regarding the seamless integration of drones into rural healthcare systems. These challenges are classified and discussed into six main groups of technical, social/human-oriented, environmental, operational, economic, and regulatory challenges. Addressing these challenges can be a basis for future research directions and facilitate using drones in rural telemedicine.

Keywords: Distributed systems and multi-agents technologies, Modeling, simulation, control and monitoring of manufacturing processes, Supply chains and networks
Abstract: Disruptions from geopolitical tensions, natural disasters, or regulations threaten the stability of production networks, necessitating greater resilience. Manufacturing-as-a-service (MaaS) offers a promising solution to remediate these challenges. A significant challenge in developing and implementing MaaS at scale is enabling the automated translation of product properties to process properties, i.e., allowing service consumers to identify appropriate service providers. Describing process capabilities underlying the services is an essential foundation to achieve this. This paper first conceptualizes process capability before proposing a model for describing process capabilities in a MaaS context. A case study of a utility sensor manufacturing company demonstrates the model’s practical application, showing its effectiveness in efficiently describing relevant process capabilities to support MaaS adoption.

Keywords: Smart manufacturing systems, Production planning and scheduling, Operations Research
Abstract: The Manufacturing-as-a-Service (MaaS) paradigm offers flexible and on-demand manufacturing services through digital platforms, including the case where customer facilities outsource tasks to providers of manufacturing resources (i.e., machines). A key challenge lies in matching the tasks and resources requested by customers with suitable providers according to their specific requirements. Within the limits of the available requirements and their level of accuracy, task-machine eligibility can be represented in various ways, such as binary values or score-based weights. These requirements are then integrated into the subsequent scheduling problem to be solved by the MaaS platform. In this paper, we extend a scheduling model by integrating task-machine eligibility score-based weights as input to the scheduling problem via eligibility level constraints. Numerical experiments are conducted on randomly generated small instances based on benchmark schemes to study the implications of the introduced task-machine eligibility levels. The quality of sequences proposed within the MaaS framework is empirically evaluated for customers and providers in both aggregate and individual manners.

Keywords: Smart manufacturing systems, Simulation technologies, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The recent rise of Digital Twins (DT) in industrial applications marks a transformative shift in how industries manage, monitor, and optimize physical assets. Digital Twins enable real-time data analysis and improved decision-making, leading to increased efficiency of their physical counterparts. Recent contributions in the literature have developed digital twins of plethora of systems, highlighting the consequent advantages in each case individually. Industrial systems are often a collection of different entities that interact and exchange material and information. Hence, the full potential of Digital Twins can only be realized if they also can interact and share data across diverse platforms and technologies. This is where interoperability becomes crucial. The term refers to the ability of different systems, technologies, and digital twin entities to work together seamlessly. Yet, achieving this remains a significant challenge. The complexity of integrating different technologies, ensuring consistent data interpretation, and safeguarding data security are among the key hurdles in developing a digital twin ecosystem. Starting from the results of the literature review, this presentation will highlight the critical role interoperability plays in unlocking the full potential of Digital Twins. It will list the key challenges in connecting digital twin entities across diverse technologies, focusing on compatibility, data harmonization, security, and quality. The presentation will also reflect on how the Asset Administration Shell (AAS) represents a promising solution for enhancing interoperability. Finally, it will provide insights into the requirements needed to enable seamless integration of digital twins, offering a roadmap for future developments in this field.

Keywords: Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes, Simulation technologies
Abstract: The development of digital twins for manufacturing lines represents a pivotal advancement towards the implementation of Industry 4.0, offering significant opportunities for process optimisation, enhanced operational efficiency, and cost reduction. This study aims to examine and articulate the challenges inherent in designing a digital twin for a flexible manufacturing line (FML). A case study is presented to detail the process of developing a digital twin for a real-world manufacturing line, with a focus on the difficulties encountered during its implementation. Key challenges identified include the complexity of system modelling, the reliance on historical data, and the requirement for sufficiently accurate models. The analysis highlights that tools such as the SIPOC diagram, value stream mapping, tooling matrices for machining equipment, and well-structured training programmes are instrumental in overcoming these challenges. This study provides valuable insights into best practices for designing digital twins for FML, emphasising the critical importance of early system integration, continuous testing, and iterative development. Furthermore, the research identifies specific areas where further investigations could advance the methodologies underpinning digital twin (DT) design within the manufacturing sector. The findings underscore the transformative potential of digital twins to enhance the efficiency, flexibility, and innovativeness of FML. However, they also highlight the necessity for continued technological and methodological advancements to address the challenges associated with their implementation.

Keywords: Production Control, Control Systems, Modeling, simulation, control and monitoring of manufacturing processes, Smart transportation
Abstract: In tire manufacturing, efficient monitoring systems are needed to detect trends and anomalies in multivariate data, enhancing product consistency and operational efficiency. This paper discusses a potential way for this monitoring through a multivariate approach by comparing Mahalanobis’ distance measure towards Euclidean one in an industrial context: starting with detecting production drifts, the analysis aims to improve trend and outlier detection responsiveness. Results suggest the Mahalanobis’ distance effectively identifies trends, improves consistency, and prevents anomalies faster than current methods, promising enhanced quality control and operational efficiency in view of a for fully automated monitoring via a digital twin system.

Keywords: Supply chains and networks, Supply Chain Management, Sustainable Manufacturing
Abstract: This paper introduces the LARG-AHP model, an innovative Analytic Hierarchy Process (AHP)-based framework designed to evaluate and integrate Lean, Agile, Resilient, and Green (LARG) performance in supply chains (SCs). The model addresses some key gaps in literature by offering a comprehensive decision-support tool that integrates the four LARG paradigms into a hierarchical structure, thus allowing for tackling complex, multicriteria decision-making problems in supply chain management (SCM). From an analysis of the literature, various performance indexes are proposed for each perspective, and their relative importance is evaluated using a weight system based on the frequency of usage of those indexes in literature; this ensures objectivity and replicability of the approach in diverse SC contexts. Overall, the proposed model study enriches the existing knowledge by: (i) addressing the underexplored application of multicriteria analysis for LARG evaluation, (ii) ensuring objectivity of the evaluation in diverse SC contexts, and (iii) offering practical flexibility to adapt to a wide range of optimization scenarios. An application example of the LARG-AHP, referring to a real supplier selection problem, is illustrated to show the effectiveness of the proposed approach.

Keywords: Supply Chain Management, Industry 4.0, Smart manufacturing systems
Abstract: This paper examines how integrating knowledge management (KM) into the metaverse can enhance supply chain resilience. Using the Analytic Hierarchy Process (AHP), the study identifies and prioritizes key supply chain challenges, including limited visibility, agility and Resilience. Metaverse technologies such as digital twins, immersive environments, and blockchain are explored as solutions for fostering real-time monitoring, predictive analytics, and collaborative problem-solving. A proposed case study simulates disruptions in a multi-tier supply chain to evaluate the impact of these technologies on decision-making efficiency, visibility, and knowledge sharing. The findings underscore the metaverse’s transformative potential for creating adaptive and resilient supply chains.

Keywords: Supply Chain Management, Smart manufacturing systems, Supply chains and networks
Abstract: An online-to-offline (O2O) coordination mechanism becomes inevitable to sustain profits or benefits for all the supply chain participants when a manufacturer introduces an online channel that competes with the offline channel of the retailer. We propose a crypto-coordination mechanism to alleviate O2O competition by delivering financial support to the retailer through the manufacturer’s crypto-token reward to achieve Pareto results. The results indicate that the crypto coordination mechanism mitigates O2O competition and benefits all supply chain participants. It promotes coordination through token rewards and ensures higher profits for all supply chain stakeholders to sustain competing channels. Notably, increasing the intensity of competition among channels drives higher profits, validating the effectiveness of the proposed coordination mechanism. The findings of this study deliver critical insights into the market dynamics under O2O competition. Additionally, they are of practical significance in enhancing coordination among competing channels. These insights contribute to literature and practice alike.

Keywords: Supply chains and networks, Supply Chain Management, Risk Management
Abstract: In today’s volatile global economy, supply chain resilience (SCRes) is essential for organizations to withstand, adapt to, and recover from disruptions such as natural disasters, geopolitical conflicts, pandemics, and cyberattacks. However, assessing SCRes remains challenging for organizations. This study describes the development of a digital SCRes assessment tool, integrating metrics for the SCRes capabilities redundancy, flexibility, collaboration, visibility and agility. By additionally including 16 contextual factors (e.g., Industry, Customer order decoupling point, Position in the SC, Power), we facilitate benchmarking with peers. Acting as a "mirror”, the tool enables organizations to evaluate their current resilience profile and initiate targeted improvements - bridging the gap between theoretical understanding and practical application.

Keywords: Risk Management, Supply Chain Management, Modelling Supply Chain Dynamics
Abstract: This study demonstrates a dynamic, weighted Supply Chain Resilience Assessment (SCRA) model using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model addresses critical gaps in existing frameworks by integrating key constructs such as supply chain vulnerabilities, capabilities, and performance. Through pilot and synthetic data, the study operationalizes resilience self-assessment, enabling tailored evaluations and real-time adaptability. Unlike static models, the SCRA model employs dynamic weighting to account for evolving market, geopolitical, and technological shifts. The findings demonstrate the model’s functionality, paving the way for future research to validate its applicability with real-world data and industry-specific case studies.

Keywords: Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes, Decision-support for human operators
Abstract: Digital twins have emerged as a cornerstone of Industry 4.0, offering powerful capabilities to simulate, analyze, and optimize physical systems through dynamic virtual counterparts. Despite demonstrated benefits across diverse domains—from healthcare and city planning to buildings, energy, education, and smart manufacturing—organizations often struggle to align solutions with end-user expectations. Common challenges include poorly defined requirements, overengineered platforms, and complex interoperability issues. This paper consolidates insights from the literature and underlines the importance of early stakeholder involvement, well-defined performance indicators, and deferring technological tool selection until the final stages of the design process. By choosing platforms and methods only after user needs are clearly articulated and validated, digital twin implementations can better match operational realities. Drawing on case studies from didactic production lines, agro-industrial bioreactors, and short-distance food supply chains, we illustrate how a user-centric, adaptive, and methodical approach—culminating in tailored technological decisions—supports more scalable, relevant, and sustainable digital twin solutions.

Keywords: Distributed systems and multi-agents technologies, Modeling, simulation, control and monitoring of manufacturing processes, Smart manufacturing systems
Abstract: The capability of Large Language Models (LLMs) and LLM agents to understand and generate natural language represents a major advance in the evolution of software agents, allowing not only opportunities for human interaction with manufacturing systems and their digital twins, but also language-dependent tasks ranging from predictive modeling to workflow analysis. This paper contrasts the capabilities of classic autonomous software agents and LLM software agents and offers a multi-agent framework for harmonizing autonomous software agents and LLM agents within a digital twin-enabled hybrid manufacturing system for the repair and restoration of damaged parts with complex forms. These multi-agent systems represent a useful paradigm to control process-driven systems, allowing both dynamic control of (re)manufacturing at a process level and human oversight and interaction at the system level.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Optimization and Control, Sustainable Manufacturing
Abstract: Digital Twin technology, which has undergone significant advancements in recent years, holds transformative potential for the manufacturing industry. The benefits it offers are diverse, with applications tailored to specific industrial sectors and organizational needs. A prominent application lies in energy optimization, where predictive consumption models enhance strategic decision-making processes. This article examines methodologies for energy consumption forecasting in complex industrial environments, integrating a state-of-the-art review with novel applied approaches. It evaluates their implementation feasibility, technical challenges, and performance outcomes, offering insights into optimizing energy management through DT-driven solutions.

Keywords: Supply chains and networks, Supply Chain Management, Industry 4.0
Abstract: The global drive for net-zero emissions has made supply chains very complex to handle, with three different types of carbon emissions: Scope 1 (direct emissions), Scope 2 (indirect emissions from energy use), and Scope 3 (value chain emissions). Reaching Net Zero requires a wide-ranging transformation of supply chain activities, from product design and energy use to transportation and waste management. However, the complexity and interconnectivity of supply chain operations mean that every decision in this transformation carries the risk of inefficiencies, disruptions, or other negative unintended consequences if not carefully planned and executed. This research addresses these challenges through a proposed conceptual framework enabled by digital twin technology. Digital twins, being digital representations of supply chain systems, provide an even more powerful solution with realtime insight and the ability to simulate different decarbonization scenarios. By mapping the emissions across all scopes, this framework integrates emissions assessment, scenario planning, and a dynamic feedback loop that continuously optimizes operations, allowing managers to evaluate the current state, to test the feasibility of strategies, and forecast the implication of changes proposed. This study shows the transformative role of digital twins in enabling informed, data-driven decisions that help the supply chains achieve net-zero goals with efficiency, resilience, and adaptability.

Keywords: Facility planning and materials handling, Simulation technologies, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Intralogistics manages material and information flows within facilities, and Industry 4.0 has introduced digital twin (DT) technology as a tool for managing complex systems. Despite its transformative potential, DT adoption in intralogistics remains limited. Thus, this paper reviews literature to identify trends and challenges in this domain. Findings reveal increasing interest in DT applications for intralogistics, especially in laboratory environments, while the integration of digital models, optimisation algorithms, and machine learning remains underexplored. The study seeks to enhance theoretical understanding of DT's impact on intralogistics and offer practical insights to help managers implement effective strategies for optimising internal logistics processes.

Keywords: Supply Chain Management, Inventory control, production planning and scheduling
Abstract: Healthcare logistics involves significant complexity due to the high cost of goods purchased and stored, as well as the uncertainty of demand linked to the variability of patients’ needs. Thus, effective inventory management could bring relevant benefits in terms of cost and quality of the service delivered. In such a context, automation is one of the most promising ways to improve warehouse processes. In this paper, the quantitative effects of implementing an automated storage system in a hospital warehouse are assessed. To this end, a dashboard of key performance indicators related to several dimensions is identified and measured. The results demonstrate that significant benefits have been obtained in terms of both quality and operational efficiency. In addition, the automated warehouse system has allowed relevant time savings for pharmacists who can be assigned to higher-value activities. Future research will assess the cost savings from efficiency gains achieved through warehouse automation.

Keywords: Facility planning and materials handling, Probabilistic & statistical models in industrial plant control, Industrial and applied mathematics for production
Abstract: Traditional models in flying-V warehouses with multiple pickup-and-delivery (P/D) points focus on minimizing the total one-way travel from each P/D point to the corresponding SKU pick locations across different aisles, effectively assuming that the picker starts as well as returns to any random P/D point. However, recent advancements in warehouse management systems (WMS) enable more flexible routing, for instance, allowing pickers to return to the closest P/D point after picking. We propose an optimal return route where the picker returns to the nearest P/D point after picking, which may not be the same as the start P/D location, and then begins the next pick from the same (returned) P/D point. Based on the latter assumptions, we aim to minimize the pick distance for the optimal return strategy, determine the best placement for the V cross-aisle, and identify the ideal number of P/D points. The results are then compared against traditional benchmark policies across different numbers of P/D points and other.

Keywords: Inventory control, production planning and scheduling, Discrete event systems in manufacturing, Production planning and scheduling
Abstract: Tools and technologies from the Industry 4.0 paradigm allow automated, reactive, and efficient ways to optimise operations management. However, effective cost reduction and efficiency improvement depend on the tight coordination and design of (i) automated warehouses, (ii) digital twins for production control, and (iii) interconnecting systems for material handling, coupled with simulation and optimisation routines. These interconnections are particularly strategic in sectors involving critical raw materials with price and availability fluctuations, uncertain customer demand, and unreliable assembly machines like the production of electric vehicles. In these cases, inventory management should not be the only driver for operation performance. This paper investigates the effects on inventory management of enhancing production control for a real assembly line of stators for electric car engines. The results show that using priority-based job sequencing increases line WIP but avoids deadlocks, reduces costs, and improves overall efficiency.

Keywords: Distributed systems and multi-agents technologies, Operations Research, Industrial and applied mathematics for production
Abstract: We analyze the problem of finding collision-free paths for multiple agents in high-rack warehouses, given orders for items to be shipped from the warehouse. In our model, the levels of the warehouse are connected by elevators performing all vertical motion while agents are confined to their respective level. Apart from showing the computational intractability of finding optimal solutions, we develop an approximation algorithm that produces solutions with provably low approximation factors in polynomial time for a large class of instances.

Keywords: Human-Automation Integration, Industry 4.0, Smart manufacturing systems
Abstract: The scope of this study is the development methodology of a Gesture Recognition System (GRS), making use of Artificial Intelligence (AI), and integrated into the Manufacturing Execution System (MES) provided in i-FAB, a learning factory in Università Carlo Cattaneo – LIUC, aiming at improving time tracking and displacement of unnecessary movement on the shop floor. Concerning the Human-Centricity pillar of the Industry 5.0 paradigm, this approach aims at enhancing well-being through the reduction of repetitive and inefficient tasks hence, making the MES systems more user-centric. The developed GRS can recognise certain hand movements related to various inputs to the MES so that users spend less time using a keyboard and touching devices. This leads to a reduction of time loss due to the time associated with tracking time and activity data which in the end optimizes production. Moreover, the system also lessens ergonomic risks that pertain to the tasks being performed, since unnecessary and repetitive movements of the operators are greatly reduced. The research findings indicate that this considerably improves the pace of completion of time and activity tracking on MES systems in a way that is designed to meet the requirements of Industry 5.0 which is focused on promoting a collaborative, safe and healthy environment.

Keywords: Decision Support System, Supply Chain Management, Smart transportation
Abstract: The increasing use of Unmanned Aerial Vehicles (UAVs) for last mile logistics (LML) has raised questions about their environmental impact. While there is growing interest in the operational efficiency of Unmanned Aerial Vehicles (UAVs), the literature lacks comprehensive studies addressing their full life cycle impact. This paper presents the structure of a decision support tool for the environmental impact assessment of delivery UAVs. The tool classifies UAVs based on an analysis of key technical characteristics, providing a baseline UAV model for estimating a Bill of Materials (BoM). Using the baseline BoM, the tool performs a Life Cycle Assessment (LCA) covering material extraction, production, and end-of-life phases. Additionally, the tool can simulate delivery scenarios to estimate emissions during the use phase. This tool aims to evaluate the life cycle impact of different UAV models adopted for LML involving different tools for covering all processes in aa life cycle perspective. This paper outlines the structure of the tool, showing its potential to fill a critical gap in existing research by providing a holistic environmental assessment of UAV in LML. This approach can support decision-making processes for designing and managing more sustainable LML solutions based on UAVs. Keywords: Environmental decision support systems, Life cycle, Unmanned aerial vehicles, Last mile logistics, data analysis and simulation.

Keywords: Knowledge management in production, Operations Research, Smart manufacturing systems
Abstract: Digital servitization marks a strategic shift for Original Equipment Manufacturers (OEMs), transitioning from product-oriented models to integrated service-based offerings. This transformation, driven by smart Product-Service Systems (PSS) and digital technologies, profoundly impacts Knowledge Management (KM). As servitization increases data and knowledge complexity across products, services, and interactions, effective KM processes become critical enablers. However, their role in digital servitization remains underexplored. Through a qualitative analysis of seven case studies, this research examines how KM processes evolve to support service management in OEMs. Findings identify three priority domains of intervention—Field Service Management, Customer Service, and Inquiry to Order— where KM optimisation is essential. The study highlights key challenges of KM in PSS delivery, offering insights to enhance KM strategies in digital servitization.

Keywords: Decision-support for human operators, Modeling, simulation, control and monitoring of manufacturing processes, Industry 4.0
Abstract: In the era of Industry 4.0 and the emerging vision of Industry 5.0, ensuring consistent product quality is crucial, particularly in complex manufacturing processes like injection molding. This study integrates Explainable AI (XAI) with time series analysis using real-world data from a German injection molding facility to enhance predictive accuracy and process interpretability. Results demonstrate that combining explainability techniques, such as SHAP, with time series features improves model performance, reducing the Mean Squared Error (MSE) from 0.01025 to 0.00251 and increasing the Rsquared from 0.9886 to 0.9972, while revealing hidden patterns in process dynamics. Global SHAP analysis identified key factors influencing quality, while local SHAP insights highlighted the role of setting parameters —those directly adjustable by operators— in mitigating deviations. Time series analysis further enhanced decision-making by enabling proactive interventions before process fluctuations compromised stability. By structuring decision-making into key steps —identifying influential parameters, prioritizing adjustable ones, and incorporating temporal insights— this study provides a roadmap for integrating XAI into quality control. The findings reinforce the value of human-centric AI, ensuring transparency and empowering operators to optimize industrial processes effectively.

Keywords: Human-Automation Integration, Industry 4.0, Robotics in manufacturing
Abstract: Work-related musculoskeletal disorders significantly impact industrial productivity and society. With the advent of Industry 5.0, the safety and well-being of human operators are again crucial for modern production systems. In this context, many innovative technologies have been developed for ergonomic purposes. Exoskeletons are used to support workers and reduce lifting jobs. Motion Capture technologies are applied to evaluate ergonomic risk in an easier, faster and less expensive way. In this paper, we evaluate the risk involved in multi-task manual lifting jobs with and without the support of an active exoskeleton through Motion Capture Technology. For this purpose, three different picking routines were performed by five different subjects in a laboratory setting with the Azure Kinect depth camera. Risk assessment was carried out through a tool based on the Azure Kinect to automatically calculate the Composite Lifting Index named AzKCLI. Results showed that the usage of the exoskeleton during multi-task manual lifting jobs had a subjective influence on each volunteer’s posture. However, the average risk related to posture did not increase.

Keywords: Production planning and scheduling, Inventory control, production planning and scheduling, Heuristic and Metaheuristics
Abstract: This study addresses a real-life problem involving an injection molding machine that manufactures different types of parts for the automotive industry. The studied problem deals with simultaneously batching and sequencing productions lots to meet demand, maintain buffer levels within specified ranges, and maximize resource utilization. Three variants of ant colony optimization algorithm (ACO) are proposed to tackle the problem. An experimental study is performed on different instances based on real-world data. The preliminary results show the ability of ACO to solve medium and large size instances corresponding to industrial-size problem which we were not be able to solve until now with the MIP models.

Keywords: Heuristic and Metaheuristics, Production planning and scheduling, Decision Support System
Abstract: The final project for the Production Engineering degree presents a computational tool designed to generate an examination schedule while addressing both hard and soft constraints. The scheduling problem, classified as NP-hard, has historically posed significant challenges, leading to diverse solutions ranging from manual scheduling methods in pre-digital times to sophisticated artificial intelligence-based mathematical approaches. In this project, a three-phase heuristic-driven methodology was developed to tackle the problem effectively.

Student preferences were identified as a critical factor in the methodology. To incorporate these, surveys were conducted to understand constraints and preferences, aiming to minimize scheduling conflicts. Additionally, close collaboration with academic assistants and the teaching unit provided insights into current operational procedures and the desired functionalities of the tool. This ensured that the developed solution aligned with institutional and student needs.

The proposed three-phase approach demonstrates a balance between deterministic and random methodologies, ensuring efficient allocation of high-priority subjects while leveraging randomness to resolve conflicts. The inclusion of weighted metrics provides a robust mechanism to evaluate and refine the generated schedules, resulting in a flexible and effective solution for exam scheduling.

Keywords: Production planning and scheduling, Inventory control, production planning and scheduling, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: This study introduces a production planning model designed to address the complexities of shift and overtime scheduling while minimizing frequent changes in production settings over short intervals. By incorporating flexible shift and overtime scheduling, the model enables companies to efficiently manage production, inventory, and backlogs while remaining responsive to fluctuating customer demands. The proposed mixed-integer linear programming model (MILP) optimizes production, inventory, and backorder costs through product-production line allocation, shift, and overtime decisions. The objective function minimizes total costs, including fixed and variable production costs, inventory holding costs, backorder costs, shift and overtime transition costs, and idle capacity penalties. Computational experiments validate the effectiveness of the model, demonstrating its ability to improve production efficiency, reduce operational costs, and adapt to dynamic demand conditions. The findings highlight the potential of the proposed approach to support efficient and flexible production planning in dynamic manufacturing environments.

Keywords: Sustainable Manufacturing, Operations Research, Quality management
Abstract: Disassembly is a mandatory step before any end-of-life product recovery strategy, whether it involves recycling, remanufacturing, or reuse. Consequently, planning and optimizing disassembly operations are essential. Although this problem has been extensively investigated, particularly in the last two decades, few studies have proposed disassembly planning and sequencing approaches based on And/Or graph models utilizing graph search techniques. In this study, we develop an algorithm that employs the Depth-First Search principle on And/Or graphs. The efficiency of the proposed graph technique, in terms of solution time, is demonstrated across several industrial-sized instances.

Keywords: Operations Research, Industrial and applied mathematics for production, Decision Support System
Abstract: In this paper, we present a bi-objective optimization approach for steelmaking, with a focus on scrap-loading. The first objective minimizes the costs associated with raw materials and energy, while the second one minimizes deviation from the required chemical composition of the final product. We analyze key metrics of scrapyard composition, such as size, similarity, and variance, and assess their impact on production outcomes. The results show that scrapyard configuration plays a crucial role in both cost and quality, with larger and optimized scrapyards leading to improved efficiency. By examining trade-offs using a lexicographic approach, this research provides valuable insights for bridging theoretical models with practical strategies aimed at achieving sustainable and cost-effective steel production.

Keywords: Sustainable Manufacturing, Modelling Supply Chain Dynamics, Supply chains and networks
Abstract: The study uses a hybrid simulation model combining system dynamics and agent-based modeling, followed by a multi-criteria decision-making method, to evaluate the role of remanufacturing in managing end-of-life electric vehicle batteries. This approach addresses the increasing battery volumes and the need for sustainable strategies by European manufacturers to comply with regulations and reduce recycling demand. Recycling, while straightforward for compliance, faces logistical challenges and limited capacity, making alternative solutions critical. Through 15 simulations across three scenarios from 2024 to 2035, considering agents like collection points and treatment facilities, the study highlights key findings: A 90% SOH threshold for remanufacturing results in a 47-Kt recycling deficit, €359 million in storage costs, and 2,089 KtCO2e emissions. Locating remanufacturers closer to collection points with a 70% SOH threshold adds 224 Kt of recycling capacity, cuts €91 million in storage costs, reduces emissions by 600 KtCO2e, and supports second-life options for 420 Kt of batteries.

Keywords: Operations Research, Optimisation Methods and Simulation Tools, Heuristic and Metaheuristics
Abstract: The European Union generates over 2.2 billion tonnes of waste annually and aims to reuse at least 60% as part of its circular economy transition. This shift focuses on resource recovery through reuse, remanufacturing, and recycling. However, challenges persist for manufacturers, including diverse product conditions and fluctuating supply chains. Manual operations dominate re- and demanufacturing, with variability in product models and conditions complicating automation. Advanced technologies like vision-based monitoring, flexible tooling, and robotic dismantling hold potential but require integration into adaptive systems. Dynamic task scheduling frameworks balancing robots and human operators are critical for economic and operational efficiency. Traditional optimization methods, designed for static scenarios, cannot handle modern multi-product environments. This research develops adaptive scheduling algorithms for multi-tool robotic systems, addressing product variability to ensure efficiency. These algorithms incorporate predictive models to manage uncertainties in task success rates, operational speeds, and resource availability, enabling real-time decision-making and reducing disruptions. A lab-scale semi-automated system serves as a validation platform, combining robotic workstations, human-operated cells, and conveyor mechanisms. This setup identifies bottlenecks like task delays and congestion, guiding layout optimization to improve throughput. The algorithms dynamically allocate tasks using inputs from diagnostics and robotic feedback, enhancing system resilience. This work integrates advanced scheduling algorithms, layout optimization, and digital twin models to advance semi-automated re- and demanufacturing systems, addressing variability and ensuring scalable performance

Keywords: Supply chains and networks, Sustainable Manufacturing, Discrete event systems in manufacturing
Abstract: This research investigates the influence of blockchain technology on remanufacturing processes within circular supply chains through discrete event simulation (DES). Two scenarios are compared: a traditional remanufacturing model and a blockchain-integrated model. Results indicate that blockchain improves traceability and increases the accuracy of production planning by ensuring the early detection of non-conforming cores. These findings underscore blockchain’s potential to enhance supply chain sustainability, transparency, and efficiency, providing valuable insights into its role in advancing circular economy practices.

Keywords: Supply Chain Management, Sustainable Manufacturing, Operations Research
Abstract: This study introduces an optimization framework for the timing of End-of-Life (EOL) product returns as an innovative strategy for managing EOL products to enhance circular supply chain (SC) performance. The proposed framework incorporates a multi-objective scenario-based mathematical model to determine optimal return timing under varying scenarios of SC capacity. The findings offer actionable insights for managers and policymakers to align SC operations with circular economy principles, addressing uncertainties in EOL product flows while achieving sustainability and resilience.

Keywords: Design and reconfiguration of manufacturing systems, Discrete event systems in manufacturing, Sustainable Manufacturing
Abstract: This paper presents a simulation-driven digital twin framework for improving smart mobile factory operations for large-scale linear infrastructure construction projects like rail, road etc. The framework integrates pre-deployment and post-deployment phases of Smart Mobile Factory. A Simulation model was built for the pre deployment phase using requirements from the industrial partner, and simulation experiments were conducted to identify bottlenecks and assess the effects of input parameter changes on output. The study compares various configurations of assembly lines, drying times, drying and curing capacities, number of shifts and forklift numbers on throughput and forklift transport distance. The results highlight that increasing drying capacity, parallel assembly lines and improved drying times significantly improve throughput and reduce forklift transportation distance in some scenarios. The best performing configurations were selected, and in the future model will be further developed to integrate real-time data with simulations to address the post deployment phase needs.

Keywords: Production Control, Control Systems, Scheduling, Smart manufacturing systems
Abstract: Accurately predicting demand is foundational for efficient scheduling and sequencing in production workflows, and integrating machine learning algorithms plays a crucial role in achieving this goal. Effective demand forecasting optimizes resource allocation and enhances overall operational efficiency, showcasing its significance in modern production planning paradigms. Therefore, this article investigates a classic production scheduling and sequencing problem. To solve the developed model, demand parameters were predicted using a machine learning method rather than relying on simple and error-prone historical data. Hence, the support vector machine algorithm was employed for demand prediction across various periods. Subsequently, the estimated demand values were incorporated as inputs into the mixed-integer nonlinear programming (MINLP) model, which was then solved. Eventually, sensitivity analyses are carried out on the model to assure efficiency and present the improvements in production system costs, reduced waiting times, minimized machine downtimes, and the assured performance with this approach.

Keywords: Robotics in manufacturing, Human-Automation Integration, Production planning and scheduling
Abstract: Guided by core values such as Human-centricity, Resilience, and Sustainability, Human-centric Production Planning and Control (H-CPPC) will reshape how Smart Industrial Products (SIPs) interact, both with each other and with human staff, requiring new capabilities compared to the previous Industri-4.0 (I-4.0) SIPs. This paper addresses this gap by identifying capabilities that SIPs need for H-CPPC. By correlating these capabilities and the challenges identified in the literature on PPC function, we propose a research agenda. Using a multi-method approach, in the Systematic Literature Review (SLR) of 16 papers, we identified 13 capabilities. Through content analysis, we outline 17 research topics. This paper contributes to academia by offering a research agenda for advancing I-5.0 while providing managers with insights into the capabilities and research priorities needed to implement H-CPPC.

Keywords: Scheduling, Transportation Systems, Smart manufacturing systems
Abstract: Additive manufacturing has brought great flexibility and responsiveness to the manufacturing industry. To further shorten the lead time, 3D printers as mobile factories have recently been introduced, enabling simultaneous production and transportation to the customer. Accordingly, we introduce a generalized production scheduling and vehicle routing problem for mobile 3D printers where (i) jobs can be batched to save printing time; (ii) sequence-dependent setup times exist between batches; (iii) vehicles may refill materials in depot. We propose a monolithic Mixed-Integer Linear Programming model solving the problem. We evaluate model's performance with respect to instance size and input parameters. Copyright © 2025 IFAC

Keywords: Production planning and scheduling, Design and reconfiguration of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Managers of production companies face uncertainties like machine breakdowns, demand fluctuations, and workforce availability. Reconfigurable Manufacturing Systems (RMS) provide a flexible solution to these challenges. This paper presents a two-stage stochastic programming model for optimizing unrelated parallel machine scheduling in the RMS, considering worker availability. The model adopts a resource flow-based framework to ensure that workers with different skills and availabilities are allocated efficiently throughout job sequences. The objective of the mathematical model is to minimize the expected makespan across multiple scenarios. Eighteen experiments were conducted to evaluate the model, in which key parameters were varied at multiple levels, including worker availability, reconfiguration time, and processing time. The Analysis of Variance (ANOVA) reveals that processing time had the most significant impact on the makespan, followed by worker availability and reconfiguration time. The results demonstrate the model’s ability to generate flexible schedules under uncertainty, offering valuable insights for enhancing the adaptability and efficiency of RMS operations.

Keywords: Inventory control, production planning and scheduling, Operations Research, Supply Chain Management
Abstract: We propose a mathematical model for planning the activities of a manufacturing company to maximize profits. The model matches production quantities and resource requirements with available finance, loans and investments over time in a scenario-based uncertain environment. The main new aspect is the inclusion of loan and investment decisions in the overall planning decision. Minor new aspect is the inclusion into the inventory management part of the complex problem of own inventory storage capacity with zero holding cost and setup costs for new products. A two-stage stochastic programming approach is used to model future uncertainty in product demands and prices, costs of new product setups, resources and inventory, and interest rates on loans and investments. Solving this problem is one of the conditions for the sustainability of a manufacturing company. The proposed model is a time-indexed Mixed-Integer Linear Programming (MILP) model.

Keywords: Sustainable Manufacturing, Design and reconfiguration of manufacturing systems
Abstract: Global sustainability targets and a changing market environment are placing increasing demands on the manufacturing industry. As a result, companies need to maintain or improve their competitiveness while implementing sustainability measures. Understanding the influence of possible sustainability elements on the achievement of company targets enables manufacturing companies to make a conscious selection of measures and to exploit synergies that can optimize and improve the company's performance on the way to a sustainable production system. This article, therefore, presents the results of an analysis of the influence of sustainability elements on relevant target dimensions in the production context.

Keywords: Sustainable Manufacturing, Industry 4.0, Transportation Systems
Abstract: The world of logistics today is interfacing with new changes aimed at automating warehouse management. Prominent among these is the use of robots, such as Autonomous Mobile Robots (AMR) and Autonomous Guided Vehicles (AGVs), to increase efficiency and productivity. However, few articles in scientific literature assess their environmental impact. This research analyses an industrial contest in the automotive sector in which an AMR was implemented to manage some components of a car. As an alternative, the implementation of an AGV to perform the same function was evaluated. Primary and secondary data were collected where possible to complete the inventory analysis of the two scenarios, and then a Life Cycle Assessment was carried out using the SimaPro software and the Ecoinvent database. The two robots were assessed according to their cradle-to-grave Global Warming Potential (GWP), starting from the extraction of raw materials to the use phase and final disposal. A sensitivity analysis then commented on the results obtained, identifying an impact range of 851 to 1174 kg CO2eq for an AGV, and between 889 and 1112 kg CO2eq for the identified case study.

Keywords: Supply Chain Management
Abstract: This study addresses the optimization of reverse logistics for PVC recycling in Wallonia, focusing on cost minimization and environmental impact reduction. Using a mixed-integer linear programming model, the research integrates transportation, inventory, and processing decisions while incorporating environmental metrics derived from Life Cycle Assessment. The results offer actionable strategies for designing sustainable supply chains, providing a foundation for further exploration to enhance flexibility and applicability in industrial settings.

Keywords: Simulation technologies, Decision Support System, Optimization and Control
Abstract: The future of agriculture is affected by several issues, such as climate change and water scarcity. Smart agriculture has been identified as the main driver to increase sustainability by combining different technologies. The proposed work aims at developing a digital twin application to manage a complex system characterized by living and non-living entities in a greenhouse with constrained devices. The development is based on an Internet of Things (IoT) architecture system with six layers (i.e., device, communication, IoT service, digital twin management, IoT process management and security layer). As a first step, the plant-soil-pot system and the irrigation network were fluid dynamically modeled using a lumped parameter simulation model. The digital model achieved a remarkable accuracy during testing and validation, with a coefficient of determination close to 87%. Furthermore, remarkable results have been achieved in real-time monitoring and control of the real counterpart and in simulation of new operating scenarios. To summarize the results, the proposed framework can deal with the management of complex systems by minimizing the amount of data exchanged and thus minimizing the power requirements.

Keywords: Robotics in manufacturing, Smart manufacturing systems
Abstract: Petri Nets (PN) is a formalism for representing and analysing concurrent systems. In this article we propose a modelling and analysis approach for production systems using PN. Basic model constructs such for entities (for example: machines, conveyors, robots) and for process flow (sequential, branching, joining) along with special cases for synchronisation, semaphores watchdogs and counters are discussed to form an engineering methodology based on these basic elements. The approach is demonstrated with the modelling and analysis of bottle filling production line.

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

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

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

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

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

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

Keywords: Industry 4.0, Risk Management, Production planning and scheduling
Abstract: Understanding the spatio-temporal dynamics of worker movements within complex factory environments, such as shipbuilding facilities, is crucial for proactively assessing safety and enhancing operations through potential process adjustments and factory layout optimizations. While existing literature offers methods to study worker movements, the detailed elicitation of movement patterns remains limited. This research proposes an analytical framework for studying spatio-temporal worker movements using Ultra-Wideband (UWB) tracking data. The framework classifies worker movements into two categories: dwell and transit, serving as the foundation for uncovering movement patterns. The study reports the movement patterns derived from data collected on six workers performing an assembly task and offers actionable recommendations to improve workplace safety and productivity.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Human-Automation Integration, Smart manufacturing systems
Abstract: Although noise impacts on human well-being have been a topic for research and societies for a long time, empirical insights regarding the impact of occupational noise in manufacturing settings is surprisingly scarce. This paper provides a comprehensive review in order to outline the state-of-the-art in this segment and identify important and worthwhile fields for future research. This is strongly connected to the concept of human-centric manufacturing as Industry 5.0 as the issue one dimension of human well-being and performance. Relevant insights are research streams and research gaps in this field.

Keywords: Design and reconfiguration of manufacturing systems, Human-Automation Integration, Decision-support for human operators
Abstract: Boreout, defined as chronic boredom, under-stimulation, and disengagement at work, is a newly ana-lyzed phenomenon with significant implications for employee well-being and performance. While ex-isting research has predominantly focused on white-collar jobs, or service-oriented jobs, little attention has been paid to its prevalence and impact within operations, with a high relevance of boreout due to monotonous and repetitive blue-collar tasks, for example in order-picking or last mile delivery. A lit-erature review synthesizes insights from nine studies, to explore the gap of research in the topic of bo-reout. The findings reveal a lack of research directly addressing boreout in operations, despite working conditions that suggest potential vulnerability to this phenomenon. This insight is extended towards a theory innovation, two application case outlines and a method outlook addressing further research is-sues in this domain.

Keywords: Inventory control, production planning and scheduling, Design and reconfiguration of manufacturing systems, Optimisation Methods and Simulation Tools
Abstract: Scheduling and configuration problems in manufacturing systems are frequently viewed as separate entities, despite their interconnectedness. This research delves into intricate scheduling management, proposing a model that augments performance through optimization. Effective optimization necessitates evaluating system performance, but intricate scheduling impedes this process. Conventional methods often fail to provide insightful analysis of the system. This study introduces a model capable of generating response curves, thereby unveiling system behaviors influenced by configuration and scheduling. The methodology is validated through the application of a specific scheduler and demonstrated in a footwear case study, highlighting its practical relevance and potential for long-term decision-making.

Keywords: Decision Support System, Knowledge management in production, Industry 4.0
Abstract: Augmenting technologies, such as augmented reality, smart tools, exoskeletons, or collaborative robots, hold significant potential to support workers in manual assembly. Due to the vast array of available technologies, selecting the most suitable option for specific processes remains challenging for companies. To address this challenge, we developed ASSYBOT, a decision-support chatbot that draws insights from a curated database of 35 detailed technology fact sheets. Each fact sheet contains key information about a respective technology. The chatbot allows users to describe shop floor processes and scenarios and uses this input to recommend suitable technologies drawn from the database. This paper presents the ASSYBOT prototype and an initial user test among managers and industry professionals who rated the tool using the Net Promoter Score. The findings highlight the chatbot’s strengths in providing user-friendly interactions and delivering helpful information compared to other sources, such as literature reviews, online searches, or generic chatbots. Overall, the results indicate that, with further refinement, chatbots like ASSYBOT could become helpful resources when searching for and selecting augmenting technologies in assembly jobs.

Keywords: Industry 4.0, Decision Support System, Production Control, Control Systems
Abstract: Increasing production throughput while maintaining the same quality and man-hours allows companies to enhance profitability or reduce final product prices and is crucial in competitive markets. Addressing production bottlenecks is among the most efficient strategies for improving throughput but presents numerous challenges during implementation. Many works in the literature aim to propose methods for tackling this issue. However, only a limited number of papers delve into the practical application of these methods and the challenges faced by industry implementers. This paper focuses on the practical application and analysis of addressing bottleneck problems through a systematic approach. Firstly, it reviews the data availability and quality for existing bottleneck detection methods in the literature. Next, we focus on a real case context, where the available measures are inter-departure times, which contain error. A procedure is proposed to recover robust inter-departure times, which can be used for bottleneck detection. Examples of bottleneck detection on synthetic and real datasets are presented.

Keywords: Industry 4.0, Monitoring, diagnosis and maintenance of manufacturing systems, Decision Support System
Abstract: Text classification is essential in industrial contexts, where vast amounts of unstructured textual data (e.g., maintenance reports, incident logs, and customer feedback) hold significant potential for insights. Natural Language Processing (NLP) tools enable the efficient processing of such data, overcoming the limitations of manual methods. This paper proposes a framework to guide the selection and adaptation of NLP solutions for classification tasks in industry, focusing on data characteristics, organizational constraints, and objectives. It reviews existing NLP tools, outlines criteria for selecting optimal solutions (e.g., explainability, computational needs, and data requirements), and discusses customization through vectorization techniques, preprocessing, and fine-tuning. The framework also highlights strategies for optimizing hyperparameters and adapting models to specific use cases. This work simplifies the adoption of NLP tools for tailored industrial applications.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Industry 4.0
Abstract: Health management and failure prediction are essential to prevent unexpected breakdowns and optimize predictive engine maintenance. This article aims to assess engine health using machine learning models (CatBoost, XGBoost, and Random Forest) trained on simulated degradation data (C-MAPSS). The goal is to simplify engine health prediction into a classification task with three states: early degradation, moderate degradation, and alert, to improve maintenance strategy, helping to minimize unexpected failures and reduce operational costs. Results indicated that CatBoost performs well compared to the other models. The article also discusses the advantages and limitations of this approach, while exploring future work to refine model accuracy and expand their industrial applicability.

Keywords: Design and reconfiguration of manufacturing systems, Optimization and Control, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The enhancement of production processes represents a crucial factor in enabling global competitiveness, particularly in light of the challenges posed by fluctuating demand, operational uncertainties, and the ripple effects inherent to interconnected supply chains. This paper presents an examination of the optimization of a furniture packaging line within a large kitchen manufacturing company. The study highlights the need to achieve a balance between efficiency and flexibility, while addressing inherent factors of workspace, operator ergonomics and productivity. A case study is conducted for the purpose of collecting and analyzing data in order to identify inefficiencies in the current packaging workflow, with a particular focus on box handling and operator activities. It is discovered that significant inefficiencies exist in the excessive distances that operators have to travel in order to retrieve boxes and in the absence of a systematic approach to matching box dimensions with furniture units. In order to address these issues, the research proposes the implementation of a mobile rack system. This solution optimizes the utilization of space, reduces the distances travelled by operators, and enhances the efficiency of the line.

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

Keywords: Supply chains and networks, Simulation technologies, Optimisation Methods and Simulation Tools
Abstract: Additive manufacturing (AM) revolutionises traditional manufacturing by enabling localised, on-demand production, reducing waste, and enhancing design flexibility. The adoption of the AM method also transforms supply chains (SCs) in several perspectives due to, removing and adding some nodes and arcs. While this transformation offers numerous benefits, it also presents significant challenges in configuring an optimal network for AM SCs, especially when a decentralization network is preferable. In this regard, this study investigates using the network optimisation modelling (NOM) method to optimise decentralised AM SCs. Utilising AnyLogistix software, the study models an AM SC to determine the optimal network configuration that minimises costs while ensuring timely deliveries. It explores the advantages of decentralised production, such as reduced lead times and costs. This study contributes to the growing body of literature by addressing gaps related to NOM in AM contexts, providing valuable insights for practical applications in SC management.

Keywords: Risk Management, Sustainable Manufacturing, Simulation technologies
Abstract: In a world increasingly impacted by supply chain disruptions and the demand for low-emission industrial districts, this study explores how additive manufacturing (AM) and industrial symbiosis (IS) can transform spare parts supply chains. Through simulation modelling, conventional and AM-supported SC configurations are compared across scenarios involving stability, disruptions, and recovery strategies. AM facilitates localised, on-demand production, improving flexibility and spare parts availability, while IS utilises waste materials to lower emissions and costs. The findings highlight that integrating AM and IS enhances supply chain resilience and sustainability, addressing global challenges and advancing circular economy practices within industrial ecosystems.

Keywords: Inventory control, production planning and scheduling, Industry 4.0
Abstract: In today’s complex industrial landscape, managing spare parts inventory is essential for ensuring operational continuity and minimizing downtime costs. However, traditional inventory management strategies often struggle to keep up with the sector’s dynamic demands. In this research, for the first time, we applied the well-known Joint Replenishment Problem (JRP) alongside Additive Manufacturing (AM) to optimize spare parts inventory management. Specifically, we benchmarked the joint replenishment of Classical Manufacturing (CM) parts with AM ones, both investigated under a periodic review policy. Notably, we found that while individual spare parts are more cost-effective with AM, joint management is most efficient with CM unless a 30% reduction in printing costs is achieved.

Keywords: Inventory control, production planning and scheduling, Risk Management, Supply Chain Management
Abstract: This paper presents an enhanced Three-Way Decision (TWD) framework designed to classify spare parts production into Additive Manufacturing (AM), Conventional Manufacturing (CM), or uncertainty, thereby improving decision-making accuracy and adaptability. The approach integrates logistic regression-based conditional probability estimation with relative loss functions that evaluate the costs associated with AM, CM, and inventory-related risks. First, logistic regression is applied to dynamically estimate the conditional probabilities of each spare part being assigned to AM or CM. Second, a relative loss matrix is calculated, which is based on the costs associated with AM, CM, and inventory-related risks. Finally, the framework calculates the expected loss for each spare part and categorizes them into three decision regions: AM, CM, or uncertainty. A case study on spare parts production validates the model’s effectiveness, demonstrating its ability to enhance risk management and decision-making precision in practical scenarios.

Keywords: Sustainable Manufacturing, Modeling, simulation, control and monitoring of manufacturing processes, Supply Chain Management
Abstract: The metal manufacturing industry has faced economic and environmental challenges in recent years. Additive manufacturing (AM) has emerged as a promising and innovative solution to address these challenges. Numerous studies have investigated the advantages of AM from a supply chain management perspective, with significant attention directed toward cost estimation for part production. However, while existing studies address various cost sources, they often overlook certain cost factors throughout the AM process. Furthermore, although the effects of process parameters on costs have been analyzed, their impact on the quality of parts has been overlooked. This oversight raises concerns about the accuracy and reliability of existing cost estimation models. To address these gaps, this study proposes a decision support system that incorporates the effects of process parameters on the quality of the produced parts, empowering managers and practitioners to determine the most cost-effective AM options.

Keywords: Design and reconfiguration of manufacturing systems, Line Design and Balancing, Human-Automation Integration
Abstract: In Industry 5.0, new manufacturing systems, commonly known as hybrid manufacturing systems, are designed by combining humans and automated technologies in shared workspaces. By adopting these systems, the benefits of both humans and automation can be achieved, ensuring flexibility and adaptability. In such a context, the technological advancement of collaborative robots has guided the transition from robotic or semi-automated assembly systems to collaborative assembly systems. The selection and balancing phase of the equipment tools are crucial to these new assembly systems. In a workplace where workers and collaborative robots collaborate, they guarantee minimal investment costs, efficiency, worker safety, and limited energy consumption. This paper focuses on Collaborative Assembly Lines (CAL) and aims to optimally solve a bi-objective model that minimizes the assembly system's total costs and energy consumption. In assessing energy consumption, one considers the variation in energy expenditure when collaborative robots are either engaged in work or idle, operating at a fixed speed. Furthermore, collaborative robots and workers can execute their tasks by adopting three strategies: independent, sequential, and supportive. The model is optimally solved for a numerical case by applying the epsilon-constraint algorithm. Finally, some managerial insights are derived from the numerical case.

Keywords: Design and reconfiguration of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes, Industry 4.0
Abstract: The integration of reconfigurable manufacturing systems (RMS) with digital twin (DT) technology presents both opportunities and complexities. Since many reconfigurations in mix-model flow lines are related to product variety, and thus product arrival from upstream station contains the information for most of the reconfiguration requirements, we propose a novel scheme for the use of workstation DTs to best handle the frequent reconfigurations. We present the concept of two alternating DT replicas for reconfigurable stations. One of these DTs (referred to as the current twin) is focused on processing the current product, while the second twin (the incoming twin) is focused on the incoming product and its process requirements. This mechanism allows the incoming twin to prepare for changes and start the reconfiguration as early as possible. Some reconfigurations may be done before the workpiece arrival, and a plan for minimizing the reconfigurations time would be ready for the switching moment between incoming and outgoing products. The hand-off process of the workpiece, as it switches stations, is reflected by real-time digital hand-off process at the station where the incoming twin and current twin interchange roles. This is a key to the improved efficiency of the process which is also the paper’s major contribution to enhancing the flexibility and responsiveness of mixed model flow lines. The paper concludes with a discussion of future research directions, including the integration of artificial intelligence for predictive reconfiguration and the extension of the digital twin concept to entire production networks.

Keywords: Design and reconfiguration of manufacturing systems, Risk Management, Line Design and Balancing
Abstract: Reliability of manufacturing systems, i.e. the analysis of their failures, is a key component to ensure the productivity and competitiveness of industrial companies. In an increasingly volatile global environment, the ability to control internal factors of uncertainty, such as system components breakdowns, is crucial. The structure of Reconfigurable Manufacturing Systems (RMS) should help in this matter, and this advantage is often mentioned, but few papers actually examine this question in depth. In this article, we discuss the link between scalability, one of the main features of RMS, and reliability and we present a new analytical method to assess their reliability. This method offers an interesting alternative to existing approaches since it is suitable for the design phase, when few data are available, and it could lead to the development of more computationally tractable procedures for reliability assessment. A didactic example illustrates its use and the conclusions that can be drawn from its application as well as future research directions.

Keywords: Design and reconfiguration of manufacturing systems, Line Design and Balancing, Human-Automation Integration
Abstract: We develop a novel mathematical approach to enhance the efficiency of assembly line with Human-Robot Collaboration (HRC). The developed model strives to reduce cycle time while considering operational limitations and budgetary factors. It tackles key challenges such as task distribution, setup durations, and the complexities of multi-station, multi-mode assembly processes. Tested across different scenarios, the model demonstrates significant improvements in workflow efficiency and resource allocation. Ultimately, this research presents a structured methodology to foster more flexible and high-performing assembly lines through optimized HRC scheduling.

Keywords: Design and reconfiguration of manufacturing systems, Decision Support System, Line Design and Balancing
Abstract: Discrete manufacturing companies are challenged to transform their existing manufacturing system to be better prepared for the changes caused by unstable supply chains and new market regulations. Reconfigurable Manufacturing Systems is a manufacturing paradigm conceived to deal with change in a fast and cost-effective way. Most of the design methods for such reconfigurable systems do not take the information of existing manufacturing systems into account. Thus, this paper proposes a method to generate reconfigurable manufacturing alternatives from legacy factories' information. The proposed method uses the bill of materials and the initial production capacity as inputs. The potential use of the proposed method is demonstrated with an illustrative example.

Keywords: Supply Chain Management, Optimisation Methods and Simulation Tools, Risk Management
Abstract: The development of renewable energy and its supply chain design is an inevitable trend nowadays. Among all the available options, renewable hydrogen, or green hydrogen, is one of the most promising ones. Although the design and optimisation of green hydrogen supply chains have begun, the consideration of supply chain resilience has not been widely included in the current literature. Further, given the special social attributes of energy, energy supply chain resilience also needs to conform to the macro energy security conceptual framework. Therefore, this paper aims to develop a performance evaluation model that can be used for the optimal design of the green hydrogen energy supply chain. The model can reflect the balance between efficiency and resilience, and can simultaneously integrate with broader energy security concerns.

Keywords: Supply Chain Management, Supply chains and networks, Risk Management
Abstract: This paper analyzes medicines shortages from a supply chain viability perspective, using the case study of the Paracetamol syrup shortage in Germany in 2022-2023. We analyze the causes leading to this shortage and show that the pharmaceutical companies have utilized the scalability adaptation strategy and have implemented digital technologies to enhance their supply chain viability. In addition, an adaptation of the regulations imposed by the supply chain ecosystem was necessary to ensure the provision of society with the required medicines in the long-term. We contribute to the theory of viable supply chains by proposing that in regulated markets such as pharmaceuticals, the adaptation of the viable supply chain ecosystem can be required to achieve supply chain viability.

Keywords: Supply chains and networks, Risk Management
Abstract: Nowadays, more and more disruptive events such as weather phenomena, health crises, or wars can affect supply chain resilience. At the same time, new circular approaches based on the recovery of waste are spreading in companies worldwide. The goal of this work is to analyze the resilience of circular supply chains under market demand spike, as happened in the Covid-19 pandemic scenario. The results obtained show how the implementation of circular paradigms, such as industrial symbiosis and closed-loops supply chains, can improve supply chain resilience. Particularly, supply chain’s adaptive and restorative capacities are always improved, also for moderate degree of circularity. Moreover, increasing the circularity of supply chain also increases the absorptive capacity.

Keywords: Supply Chain Management, Supply chains and networks, Risk Management
Abstract: This study examines the interplay between supply chain resilience and working capital practices, highlighting a critical yet underexplored gap in how these strategies can either align or conflict. It examines how companies manage trade-offs between these domains, focusing on dependency and certainty in buyer-supplier relationships. Findings show that strong internal alignment between finance and supply chain functions is critical for effective management. Dependency and certainty levels drive strategic choices, with low levels favoring efficiency-oriented practices and high levels enabling collaborative approaches. This research provides valuable insights for managers navigating supply chain disruptions, addressing a crucial gap in understanding the balance between liquidity and resilience strategies.

Keywords: Supply Chain Management
Abstract: Supply chains face persistent tensions between resilience and efficiency, particularly in the manufacturing and logistics sector, where external disruptions and resource constraints exacerbate conflicting objectives. While prior research has largely focused on technology-driven solutions, the critical role of individual mindsets remains underexplored. This study investigates how a paradox mindset, the ability to embrace and integrate competing demands, enables individuals to align resilience and efficiency. Based on 18 interviews with supply chain managers and employees across Europe, the findings reveal that individuals with a paradox mindset recognize tensions as opportunities for innovation, supported by human-centric enablers. These include personal foundations, such as trust and psychological safety, and environmental catalysts, like collaboration and autonomy. This research advances paradox theory by applying it to supply chain management and highlights actionable insights for fostering adaptive, human-centric supply chains capable of navigating both short-term disruptions and long-term transformations.

Keywords: Design and reconfiguration of manufacturing systems, Production planning and scheduling, Sustainable Manufacturing
Abstract: The emergence of reconfigurable manufacturing offers innovative solutions for efficiently adapting to changing market demands and system modifications. This paper introduces a robust possibilistic programming framework to address a multi-objective production scheduling problem within sustainable reconfigurable manufacturing systems, incorporating uncertainty. The model captures the workforce learning effect on reconfiguration times, aiming to minimize makespan, production costs, and social sustainability while considering uncertain parameters. Possibilistic chance-constrained programming and robust possibilistic programming approaches are applied to assess both model and solution robustness. Additionally, the framework addresses workplace safety risks linked to workforce assignments and incorporates workforce entry preferences for flexible hours. By considering the learning effect in reconfiguration times, the model reflects the dynamic nature of scheduling, aligning more closely with real-world scenarios. The augmented epsilon-constraint method is also used to efficiently find Pareto-optimal solutions for the multi-objective model.

Keywords: Design and reconfiguration of manufacturing systems, Line Design and Balancing, Operations Research
Abstract: This paper presents a novel bi-objective mixed-integer linear programming (MILP) model for workforce management and resource selection. The model accounts for technician training, and it considers uncertainty in the availability of technicians. The primary objective is to minimize the total costs associated with resources and technician training, and a second objective maximizes fairness in preferred skill acquisition across workers. We employ the AUGMECON method to find Pareto optimal solutions. We perform computational experiments with IBM ILOG CPLEX, and the results highlight the trade-offs between cost and fairness under various system sizes and constraints. Additionally, the paper explores two different fairness metrics—individual fairness and collective fairness—and compares their impacts on resource selection, technician costs, and fairness outcomes. The findings suggest that while maximizing fairness leads to higher costs, a balanced approach can offer effective solutions for real-world applications.

Keywords: Smart manufacturing systems, Production planning and scheduling, Scheduling
Abstract: Scheduling in wafer fabrication systems presents a significant challenge, particularly due to dynamic wafer arrivals, reprocessing, and complex constraints such as capacity limits and process incompatibilities. This paper proposes a novel deep reinforcement learning approach that integrates adversarial learning with multi-agent collaboration to address these challenges. We introduce two types of agents: group batch agents and batch assignment agents, both utilizing Long Short-Term Memory networks to capture dynamic system states and improve decision-making. To enhance training efficiency, adversarial learning is leveraged to generate a diverse set of training data by combining real and simulated experiences. Additionally, we propose a relationship correction network to capture internal dependencies within the RL process, which is optimized using relative entropy. Our approach accelerates the convergence of scheduling decisions, significantly improving both performance and adaptability. Experimental results demonstrate that our method outperforms traditional RL and heuristic approaches, reducing wafer flow time and enhancing scheduling accuracy.

Keywords: Supply chains and networks, Supply Chain Management, Smart manufacturing systems
Abstract: This paper investigates a process model for implementing Smart Digital Tracking within a supply chain (SC) aligned with Lean strategy. The Lean approach emphasizes value creation by focusing efforts on the implementation of Smart Digital Tracking. The study identifies critical criteria for selecting an appropriate Proof of Concept (PoC) and a suitable customer for initial implementation, setting the foundation for scaling the digital tracking system across a broader product portfolio and customer base within the SC. In addition to highlighting the strategic significance of digitalization as the future of SC management, this study provides a Process Implementation Model to guide decision-makers in adoption digital tracking systems. The proposed methodology, inspired by kaizen, incorporates an advanced PDCA cycle designed for Lean 4.0 projects, with a focus on integrating simulation capabilities during the implementation of new digital solutions. The paper concludes with a case study from the aerospace industry, showcasing a company’s efforts to implement a digital tracking solution while simulating various scenarios. The study addresses the company’s challenge of selecting the optimal PoC and determining the most appropriate customer for the initial deployment. Furthermore, the proposed framework fosters and open collaboration by enabling the testing of various delivery reconfigurations supported with Digital Tracking.

Keywords: Production planning and scheduling, Industry 4.0, Decision-support for human operators
Abstract: This study focuses on dynamic scheduling in a real Flow Shop assembly process, addressing specific constraints of a brass manufacturing system. A Mixed Integer Linear Programming (MILP) model is proposed to be integrated into an existing Decision Making System (DMS) developed in previous works. This approach aims to enhance Digital Twin (DT)-driven dynamic scheduling for Industry 4.0 by addressing uncertainties related to machine availability. The MILP model will be coupled with a 3D simulation platform within a Cyber Physical Production System (CPPS), enabling adaptive rescheduling in response to machine disruptions and unforeseen events. Validation scenarios based on real-case data illustrate the potential of this integration to effectively tackle dynamic scheduling challenges.

Keywords: Decision Support System, Optimisation Methods and Simulation Tools, Sustainable Manufacturing
Abstract: Selecting appropriate packaging materials is one of the crucial steps in product quality, safety, and sustainability. A decision-making framework, implemented in Microsoft Excel, has been developed to assist the selection based on the user's questions and the characteristics of available materials. The framework is based on 13 key useful to guide users in materials selection. Responses were mapped against three key material attributes-oxygen barrier (O₂TR ), water vapor barrier (WVTR), and mechanical strength with five levels (from very high to very low). This involved the development of three database respectively; (i) material dataset which include a set of polymeric materials; (ii) materials characteristics and the responses database which server as back end interface of the developed tool (iii) level definition dataset, in which is define the level of each materials from very high to very low depending on the attributes values for O₂TR ,WVTR and mechanical strength. It gives points to the material for the match of user requirement-material attribute and sums them up to reach the most appropriate option. The system logic uses Excel functions, conditional statements, and dynamic tables to ensure ease of access and use. Validation was conducted through practical scenarios with a realistic dataset of polymeric materials, demonstrating the tool's accuracy and utility. The approach offers scalability and can be extended for integration with automated systems or expanded databases, making it a versatile solution for various packaging selection needs.

Keywords: Sustainable Manufacturing, Industry 4.0, Decision Support System
Abstract: The rapid advancement of digital technologies is reshaping manufacturing, enabling smarter, interconnected systems that redefine industrial processes. While frameworks like the Reference Architectural Model for Industry 4.0 (RAMI 4.0) provide essential guidance for navigating this digital transformation, they overlook a fundamental aspect: the sustainability of processes. Traditionally, environmental impacts are assessed through Life Cycle Assessment (LCA), a well-established methodology. However, LCA’s static and retrospective nature struggles to align with the dynamic, real-time capabilities of Industry 4.0. To address this dual challenge, leveraging digitalization while ensuring sustainability, we propose integrating an Environmental Assessment (EA) layer into RAMI 4.0. This additional layer would enable real-time updates of environmental data, supporting continuous optimization of materials and production processes with a strong focus on their ecological implications.

Keywords: Design and reconfiguration of manufacturing systems, Smart manufacturing systems, Sustainable Manufacturing
Abstract: Energy sustainability is critical for manufacturing competitiveness, with industrial sectors seeking innovative approaches to reduce energy consumption. This study extends the Extended Marginal Distribution Analysis (EMDA) model by incorporating a comprehensive energy consumption evaluation framework, aiming to transform how production systems understand and optimise resource utilisation. The enhanced EMDA model introduces a novel approach to mapping energy states across reconfigurable production systems, enabling a more nuanced understanding of machine performance and power dynamics. By integrating energy consumption metrics into traditional performance analysis, the research navigates the complex trade-offs between productivity and energy efficiency.The approach aspires to establish a pathway towards more intelligent resource management in reconfigurable manufacturing environments, offering a transparent methodology for balancing operational performance and sustainable practices.

Keywords: Design and reconfiguration of manufacturing systems, Facility planning and materials handling, Sustainable Manufacturing
Abstract: Multi-deep automated storage and retrieval systems (AS/RS) are widely used in the industry. They offer more space-efficient storage of loads compared to single-deep storage systems but have the disadvantage that not all loads can be directly accessed by the storage and retrieval machine but blocking loads must be relocated first. In the planning phase of such multi-deep AS/RS the two main performance indicators are the throughput capacity which is interlinked with the cycle time and the energy consumption of each command cycle. This study introduces an analytical method for calculating the energy consumption and cycle time of a multi-deep automated storage and retrieval system (AS/RS). We show calculation models for four distinct operating strategies based on operating strategies available in the literature. The calculation models are validated by discrete event simulation and compared to find the cycle time and energy consumption minimizing operating strategies. We find that strategies which try to minimize the travel distance are preferred over strategies which try to minimize the number of relocations. Another important result is that cycle time minimizing layouts of multi-deep AS/RS are not identical to energy consumption minimizing layouts.

Keywords: Inventory control, production planning and scheduling, Industry 4.0
Abstract: The advancements in the manufacturing sector have necessitated the evolution of educational paradigms to address skills mismatches and enhance workforce adaptability. This paper explores the potential learning use cases related to the incorporation of automated warehouse stations into Learning Factories (LFs) to develop competencies for warehouse management within Industry 5.0 framework. It presents a structured methodology for aligning educational goals with industry demands, emphasizing technical, methodological, personal, and interpersonal skills. A detailed analysis of the skills required for warehouse managers was conducted using the ESCO database and scientific literature. These findings informed the development of a laboratory use case involving an automated warehouse station at the University of Bergamo's SLIM Lab. The study evaluates various warehouse management strategies, emphasizing the relationship between operational choices and key performance indicators (KPIs) like energy consumption, error rates, and processing times. Results highlight the effectiveness of LFs in fostering essential competencies while addressing limitations in current LF configurations, such as inconsistencies in data collection and KPI measurement. The proposed framework demonstrates potential for broader applications across diverse job profiles and LF setups, providing a robust foundation for advancing education and training in automated logistics systems. Future research aims to extend this framework to encompass additional job profiles and advanced LF technologies.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Sustainable Manufacturing, Decision-support for human operators
Abstract: This paper investigates the integration of carbon accounting into Digital Twin (DT) technology, providing a dynamic and granular approach to monitoring and optimizing environmental impacts in warehouses. Traditional carbon accounting methods, often rely on static, aggregated data, limiting their effectiveness for real-time operational decision-making. By embedding carbon accounting within DTs, this research enables precise tracking of environmental impacts across specific warehouse resources, including energy consumption, material handling, and employee commuting. Comparative analysis highlights the DT's ability to address operational-level challenges, complementing the broader strategic insights offered by conventional methods. Initial simulations reveal key emission contributors, such as commuting and plastic use during peak seasons, demonstrating DTs' potential to drive targeted sustainability actions. Despite challenges like data dependency and implementation costs, DTs offer real-time, resource-specific insights that significantly advance sustainable warehousing practices, bridges operational and strategic management and provides holistic and informed decision-making.

Keywords: Robotics in manufacturing, Simulation technologies, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The increasing demands of e-commerce and challenges in modern warehousing have emphasized the need for advanced robotic solutions in intralogistics, particularly for order picking tasks. This study investigates the integration of robotic bin-picking systems with adaptive detection times and multi-gripper coordination to enhance the efficiency of automated order preparation processes. Through controlled laboratory experiments with an UR5e robotic arm and three distinct robotic grippers—two-finger, soft, and vacuum—empirical data on detection times and success rates were collected under varying conditions. This data was utilized to generate a synthetic dataset that simulated realistic operational scenarios involving conveyor-based transportation of SKUs from automated storage and retrieval systems AS/RS and separate conveyors for order totes. A simulation model, developed in AnyLogic, evaluated two operational strategies: minimizing detection times versus minimizing gripper exchanges. Results indicate that while the scenario minimizing gripper exchanges reduced the total number of gripper swaps by 26.5%, the improvement in average cycle time was modest, with a 2.9% reduction. These findings highlight the marginal time difference between the two strategies, suggesting that system priorities such as mechanical wear or operational robustness could guide strategy selection.

Keywords: Sustainable Manufacturing, Inventory control, production planning and scheduling, Supply Chain Management
Abstract: This study aims to support sustainability in warehouse management and order picking by integrating a simulation tool for evaluating operational performance with sustainability KPIs encompassing economic, environmental, and social dimensions. By analyzing two routing policies under various demand and layout scenarios, this article links standard operational metrics, such as travelled distance, with sustainability indicators, such as energy consumption, reduction in CO2 emissions and working times. The findings demonstrate the advantages of S-Shaped Advanced policy in heterogeneous demand environments. Finally, this study introduces a practical approach for optimizing warehouse management while addressing sustainability, promoting multidimensional performance evaluation of industrial operations.

Keywords: Optimisation Methods and Simulation Tools, Modeling, simulation, control and monitoring of manufacturing processes, Robotics in manufacturing
Abstract: This paper presents a digital model-driven optimization framework for addressing real-world constraints in robot-assisted palletization, focusing on applications in warehousing. The proposed approach integrates 3D Bin Packing Problem with robotic placement constraints, ensuring feasible and optimal palletization solutions. By leveraging a digital model of the robotic system, the method captures critical factors such as reachability, collision avoidance, and dynamic stability, which are often overlooked in traditional optimization models. A custom genetic algorithm drives the optimization process, balancing space utilization and operational feasibility. Experimental results demonstrate the robustness of the framework in handling complex, real-world logistics scenarios while ensuring compatibility with robotic systems. This research bridges the gap between theoretical optimization and industrial implementation, offering a scalable solution for resilient and efficient warehousing automation.