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Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC., CC BY 4.0 DEEDCorresponding author: Email: amir.nourmohammadi@his.seThis study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the VF-KDO, ACCURATE 4.0, and PREFER projects.

The performance of a production system is primarily evaluated by its throughput, which is constrained by throughput bottlenecks. Thus, bottleneck analysis (BA), encompassing bottleneck identification, diagnosis, prediction, and prescription, is a crucial analytical process contributing to the success of manufacturing industries. Nevertheless, BA requires a substantial quantity of information from the manufacturing system, making it a data-intensive task. Based on the dynamic nature of bottlenecks, the optimal strategy for BA entails making well-informed decisions in real-time and executing necessary modifications accordingly. The efficient implementation of BA requires gathering, storing, analyzing, and illustrating data from the shop floor. Utilizing Industry 4.0 technologies, such as cyber-physical systems and cloud technology, facilitates the execution of data-intensive operations for the successful management of BA in real-world settings. The main objective of this study is to establish a framework for BA through the utilization of Cloud-Based Cyber-Physical Systems (CB-CPSs). First, a literature review was conducted to identify relevant research and current applications of CB-CPSs in BA. Using the results of the review, a CB-CPSs framework was subsequently introduced for BA. The application of the framework was assessed via simulation in a real-world manufacturer of marine engines. The findings indicate that the implementation of CB-CPSs can contribute significantly to throughput improvement., CC BY-NC-ND 4.0 DEED© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Correspondence Address: E. Mahmoodi; Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, Skövde, 54128, Sweden; email: Ehsan.mahmoodi@his.seWe would like to express our gratitude to the Knowledge Foundation (KKS), Sweden, for their financial support through the ACCURATE 4.0 project, under grant agreement No. 20200181. We also wish to extend our appreciation to our industrial partner, Volvo Penta, Sweden. Their collaboration, expertise, and invaluable insights have significantly contributed to this study., ACCURATE 4.0

Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions., CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: S. Lidberg; Högskolan i Skövde, Högskolevägen, Skövde, Box 408, Sweden; email: simon.lidberg@his.se

Group decision making is traditionally a human-centered process, where communication, synchronization and agreement are driven by the stakeholders involved. In the area of multi-objective optimization (MOO), this becomes a challenge, because MOO usually produces a large amount of trade-off solutions that need to be analyzed and discussed by the stakeholders. Moreover, for transparent group decision making, it is important that each decision maker is able to trace the entire decision process – from associated data and models to problem formulation and solution generation, as well as to the preferences and analyses of other decision makers. A graph database is capable of capturing such diverse information in the form of a knowledge graph. It can be used to store and query all dependencies and hence can support complex decision-making tasks. Further advantages are the inherent suitability for visualization and the possibilities for pattern matching, graph analytics and, if semantically enriched, to infer new connections in the graph. In this paper, we show how such a knowledge graph can be used to support more transparent and traceable decision-making activities, particularly when multiple stakeholders with differing preferences or perspectives are involved., CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: L. Mittermeier; University of Skövde, School of Engineering Science, Sweden; email: ludwig.mittermeier@his.se

In today's uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various whatif improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered., CC BY 4.0Corresponding author. E-mail address: amir.nourmohammadi@his.se (A. Nourmohammadi).This study is supported by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the VF-KDO, ACCURATE 4.0, and PREFER Projects. The authors highly appreciate the valuable collaborations with the experts from the industrial partner of this research.

The realisation of a successful product requires collaboration between developers andproducers, taking account of stakeholder value, reinforcing the contribution of industry tosociety and enhancing the wellbeing of workers while respecting planetary boundaries.Founded in 2006, the Swedish Production Academy (SPA) aims to drive and developproduction research and education and to increase cooperation within the production area.SPA initiated and hosts the conference Swedish Production Symposium. This specialissue is based on invited papers from the 10th Swedish Production Symposium(SPS2022), held in Skövde, Sweden, from 26–29 April 2022. The overall theme forSPS2022 was ‘Industry 5.0 transformation – towards a sustainable, human-centric, andresilient production’.As stated by the European Commission the vision of Industry 5.0 recognises societalgoals. It goes beyond a techno-economic vision, industrial value chains and growthaiming for the industry to become a resilient provider of prosperity, respecting ourplanets boundaries, and placing the industrial worker, her well-being, at the centre of theproduction process.In this special issue, we set out to explore the transition to a resilient, sustainable andhuman centric industry. The first paper explores the need for a joint strategical vision thatinclude technology (selection, development, and implementation), organisation(structure, agility, management, stakeholder collaborations, work environment) andpeople (skills and competences, participation, innovation and creative collaborativeculture, and change readiness), to achieve a resilient and sustainable production systemeffectively and efficiently. The second paper discusses how reconfigurable manufacturingsystems can enable sustainable manufacturing and circularity, achieving highresponsiveness and cost efficiency. The third paper, a synthesis of universal workplacedesign in assembly, explores how human assembly workplaces can be designed in abetter way in regard to in

This work was funded by the Knowledge Foundation (KKS), Sweden, through the KKS Profile, Virtual Factories with Knowledge-Driven Optimization (VF-KDO), Grant No. 2018-0011.

The authors would like to acknowledge the support of Swedish Innovation Agency (VINNOVA) and its funding program, Produktion2030. This study is part of the Explainable and Learning Production Logistics by Artificial Intelligence (EXPLAIN) project.

Virtual engineering increases the rate of and diversity of models being created; hence requires maintenance in a product lifecycle management (PLM) system. This also induces the need to understand their creation contexts, known as historical or provenance information, to reuse the models in other engineering projects. PLM systems are specifically designed to manage product- and production-related data. However, they are less capable of handling the knowledge about the contexts of the models without an appropriate extension. Therefore, this research proposes an extension to PLM systems by designing a new information model to contain virtual models, their related data and knowledge generated from them through various engineering activities so that they can be effectively used to manage historical information related to all these virtual factory artifacts. Such an information model is designed to support a new Virtual Engineering ontology for capturing and representing virtual models and engineering activities, tightly integrated with an extended provenance model based on the W7 model. In addition, this paper presents how an application prototype, called Manage-Links, has been implemented with these extended PLM concepts and then used in several virtual manufacturing activities in an automotive company., CC BY 4.0Available online 2 June 2022, 100369Corresponding author: Iman MorshedzadehThis work was supported by the Knowledge Foundation (KKS) through the IPSI Research School at the University of Skövde [grant number 20140330], and VF-KDO Profile research project [grant number 20180011].

The use of virtual commissioning has increased in the last decade, but there are still challenges before the software code validation method is widespread in use. One of the extensions to virtual commissioning is the digital twin technology to allow for further improved accuracy. The aim of this paper is to review existing standards and approaches to developing virtual commissioning, through a literature review and interviews with experts in the industry. First, the definitions and classifications related to virtual commissioning and digital twins are reviewed, followed by, the approaches for the development of virtual commissioning and digital twins reported in the literature are explored. Then, in three interviews with experts of varying backgrounds and competencies, the views of the virtual technologies are assessed to provide new insight for the industry. The findings of the literature review and interviews are, among others, the apparent need for standardisation in the field and that a sought-after standard in the form of ISO 23247-1 is underway. The key finding of this paper is that digital twin is a concept with a promising future in combination with other technologies of Industry 4.0. We also outline the challenges and possibilities of virtual commissioning and the digital twin and could be used as a starting point for further research in standardisations and improvements sprung from the new standard., CC BY-NC 4.0Corresponding Author, Anton Lidell, University of Skövde, Sweden, E-mail: antonlidell@live.se

Simulation and optimization enables companies to take decision based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, it can be difficult to visualize and extract knowledge from the large amounts of data generated by a many-objective optimization genetic algorithm, especially with conflicting objectives. Existing tools offer capabilities for extracting knowledge in the form of clusters, rules, and connections. Although powerful, most existing software is proprietary and is therefore difficult to obtain, modify, and deploy, as well as for facilitating a reproducible workflow. We propose an open-source web-based application using commonly available packages in the R programming language to extract knowledge from data generated from simulation-based optimization. This application is then verified by replicating the experimental methodology of a peer-reviewed paper on knowledge extraction. Finally, further work is also discussed, focusing on method improvements and reproducible results., CC BY-NC 4.0Corresponding Author: Simon Lidberg, Högskolevägen, BOX 1231, Skövde, Sweden; E-mail: simon.lidberg@his.se

Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations., CC BY-NC-ND 4.0Corresponding author: Amir NourmohammadiEdited by Emanuele Carpanzano, Claudio Boër, Anna ValenteThis study is funded by the Knowledge Foundation (KKS), Sweden, through the VF-KDO and ACCURATE 4.0 projects at the University of Skövde, Sweden., VF-KDO, ACCURATE 4.0

The realization of a successful product requires collaboration between developers and producers, taking account of stakeholder value, reinforcing the contribution of industry to society and enhancing the wellbeing of workers while respecting planetary boundaries. Founded in 2006, the Swedish Production Academy (SPA) aims to drive and develop production research and education and to increase cooperation within the production area. This book presents the proceedings of the 10th Swedish Production Symposium (SPS2022), held in Skövde, Sweden, from 26-29 April 2022. The overall theme of the symposium was ‘Industry 5.0 Transformation – Towards a Sustainable, Human-Centric, and Resilient Production’. Since its inception in 2007, the purpose of SPS has been to facilitate an event at which members and interested participants from industry and academia can meet to exchange ideas. The 69 papers accepted for presentation here are grouped into ten sections: resource-efficient production; flexible production; humans in the production system; circular production systems and maintenance; integrated product and production development; industrial optimization and decision-making; cyber-physical production systems and digital twins; innovative production processes and additive manufacturing; smart and resilient supply chains; and linking research and education. Also included are three sections covering the Special Sessions at SPS2022: artificial intelligence and industrial analytics in industry 4.0; development of resilient and sustainable production systems; and boundary crossing and boundary objects in product and production development. The book will be of interest to all those involved in the development and production of future products., CC BY-NC 4.0This book is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

Globalization and mass customization are commonly translated into increased levels of complexity in manufacturing systems. One of the main reasons is the increased number of variables, parameters, and interrelations on the shop floor. This intrinsic complexity can grow exponentially when considering the manufacture of large-size products with high levels of variability and variants: the mass production of large recreational motorboats with high levels of customization and low production volumes, mass customization. With the increasing role of sustainability and concepts of Industry 5.0, focusing not just on improving production systems but also human wellbeing, quick decision making becomes essential. Data and digitalization are becoming the cornerstone for system improvement, and digital data availability and analysis can facilitate the utilization of computerized tools to support decision making and maximize the performance of complex systems. For that purpose, simulation can be a powerful analytical tool to design, maintain, and improve complex manufacturing systems. Simulation techniques usually allow handling the size and complexity commonly associated with manufacturing systems. However, in systems with highly customized and large-size products, manual processes, and limited floor space, the implementation of simulation techniques is not straightforward, especially considering the aspects of variability, data collection, model validation, and system reconfiguration. With a particular focus on large-size products and limitations of a constrained existing facility layout, this paper presents the implementation of a simulation-based reconfiguration assessment considering manual production, assembly, and internal logistics requirements. Going through an industrial case study of large recreational motorboats manufacturing, the paper analyses the system analysis, data collection, implementation, and validation of the methodology step by step. Considering different w, CC BY-NC 4.0Corresponding Author, Enrique Ruiz Zúñiga, JSPS Research Fellow, Systems Design Laboratory, Kyoto University, Kyoto, 615-8540, Japan; Department of Intelligent Automation, University of Skövde, Högskolevägen, Box 408, Skövde, 541 28, Sweden; E-mail: enrique.ruiz.zuniga@his.

In a Cyber-physical system, the information flow from the cyber part to the physical part plays a crucial role. This paper presents the work of development and initial testing of an augmented reality approach to provide a user interface for operators that could be a part of a robotic production system. The solution is distributed and includes a communication hub that allows the exchange of data and information between multiple clients e.g. robot controllers, an optimization platform, and visualization devices. The main contributions of the presented work are visualization of optimization results and visualization of information obtained from the robot controller and the integrated communication framework. The paper also presents challenges faced during the development work and opportunities related to the presented approach. The implemented interface uses HoloLens 2 mixed reality device to visualize in real-time information obtained from a robot controller as well as from simulation. Information regarding the placement of work objects and targets or currently executed lines of code can be useful for robotic cell programmers and commissioning teams to validate robot programs and to select more optimal solutions toward sustainable manufacturing. The operator can simulate the execution of the robot program and visualize it by overlying the robot cell with the 3D model of the simulated robot. Moreover, visualization of future robot motion could support human-robot collaboration. Furthermore, the interface allows providing the user with details from multi-objective optimization performed on a digital twin of the robotic cell with the aim to reduce cycle time and energy consumption. It allows visualizing selected scenarios to support decision-making by allowing comparison of proposed solutions and the initial one. The visualization includes cell layout, robot path, cycle time, robot energy consumption. The presented approach is demonstrated in industry-inspired cases and, CC BY-NC 4.0Bernard Schmidt, Corresponding author, School of Engineering Science, University of Skövde, PO Box 408, 541 28 Skövde, Sweden; E-mail: bernard.schmidt@his.se.This work was partially financed by the Knowledge Foundation (KKS), Sweden, through the Virtual Factory with Knowledge-Driven Optimization profile (2018-2026), and ACCURATE project (2021-2025)., ACCURATE

In light of the Industry 5.0 trend towards human-centric and resilient industries, human-robot collaboration (HRC) assembly lines can be used to enhance productivity and workers’ well-being, provided that the optimal allocation of tasks and available resources can be determined. This study investigates the assembly line balancing problem (ALBP), considering HRC. This problem, abbreviated ALBP-HRC, arises in advanced manufacturing systems, where humans and collaborative robots share the same workplace and can simultaneously perform tasks in parallel or in collaboration. Driven by the need to solve the more complex assembly line-balancing problems found in the automotive industry, this study aims to address the ALBP-HRC with the cycle time and the number of operators (humans and robots) as the primary and secondary objective, respectively. In addition to the traditional ALBP constraints, the human and robot characteristics, in terms of task times, allowing multiple humans and robots at stations, and their joint/collaborative tasks are formulated into a new mixed-integer linear programming (MILP) model. A neighborhood-search simulated annealing (SA) is proposed with customized solution representation and neighborhood search operators designed to fit into the problem characteristics. Furthermore, the proposed SA features an adaptive neighborhood selection mechanism that enables the SA to utilize its exploration history to dynamically choose appropriate neighborhood operators as the search evolves. The proposed MILP and SA are implemented on real cases taken from the automotive industry where stations are designed for HRC. The computational results over different problems show that the adaptive SA produces promising solutions compared to the MILP and other swarm intelligence algorithms, namely genetic algorithm, particle swarm optimization, and artificial bee colony. The comparisons of human/robot versus HRC settings in the case study indicate significant improvement in

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today's manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency., CC BY-NC-ND 4.0Received 30 December 2021, Revised 9 March 2022, Accepted 15 March 2022, Available online 18 March 2022, Version of Record 2 April 2022.The co-authors would like to acknowledge the Knowledge Foundation (KKS), Sweden, for their funding through the research profile Virtual Factories with Knowledge-Driven Optimization at the University of Skövde which is partially related to this work.

Due to the nature of today's manufacturing industry, where enterprises are subjected to frequent changes and volatile markets, reconfigurable manufacturing systems (RMS) are crucial when addressing ramp-up and ramp-down scenarios derived from, among other challenges, increasingly shortened product lifecycles. Applying simulation-based optimization techniques to their designs under different production volume scenarios has become valuable when RMS becomes more complex. Apart from proposing the optimal solutions subject to various production volume changes, decision-makers can extract propositional knowledge to better understand the RMS design and support their decision-making through a knowledge discovery method by combining simulation-based optimization and data mining techniques. In particular, this study applies a novel flexible pattern mining algorithm to conduct post-optimality analysis on multi-dimensional, multi-objective optimization datasets from an industrial-inspired application to discover the rules regarding how the tasks are assigned to the workstations constitute reasonable solutions for scalable RMS., © 2022 IEEEIntelligent Production Systems Division, University of SkövdeThe authors gratefully acknowledge the Knowledge Foundation (KK-Stiftelsen), Sweden, for their upport and provision of research funding through the research profile Virtual Factories Knowledge-Driven Optimization (VF-KDO) at the University of Skövde, Sweden, in which this work is a part of it.

Manufacturing data and information are produced and used during the lifecycle of product development. Product lifecycle management (PLM) systems provide a suitable platform for managing them. For appropriate management of manufacturing data, it needs to be identified, classified, and stored based on the structure of PLM systems. In this paper, the results of an industrial manufacturing data collection study are interpreted, and their relation to the main structures in PLM systems is specified. Subsequently, a new information model for assigning this data and information to the PLM data model is presented. The main contribution of this information model is the definition of property and change objects and integrating them with the structure of PLM systems; changes and revisions of those data are formally defined and hence traceable., Online publication date: Fri, 08-Oct-2021The full text of this article is only available to individual subscribers or to users at subscribing institutions., KDDS, VF-KDO

In today's global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today's manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.

The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker's preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.

In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach., CC BY 4.0This work was partially supported by the Knowledge Foundation (KKS), Sweden, through the funding of the research profile VirtualFactories with Knowledge-Driven Optimization (VF-KDO) (2018-2026)., VF-KDO

Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies’ success in the current fiercely competitive market. Despite the RMS’s advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers’ capacity, and minimizing the number of machines, are optimized simultaneously while considering the system’s stochastic behavior to achieve the desired system’s configuration., CC BY-NC-ND 4.0Corresponding author Tel.: +46-500-448-586. E-mail address: carlos.alberto.barrera.diaz@his.seEdited by Dimitris Mourtzis

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool-indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision-makers to manage their tools more efficiently compared to current practices.

Decentralised in-house logistics areas, known as supermarkets, are widely used in the manufacturing industry for parts feeding to assembly lines. In contrary to the literature and inspired by observation in a real case, this study relaxes the assumption of using identical transport vehicles when deciding on the supermarkets’ location by considering the availability of different vehicles. In this regard, this study deals with the integrated supermarket location and transport vehicles selection problems (SLTVSP). A mixed-integer programming (MIP) model of the problem is developed. Due to the complexity of the problem, a hybrid genetic algorithm (GA) with variable neighborhood search (GA-VNS) is also proposed to address large-sized problems. The performance of GA-VNS is compared against the MIP, the basic GA, and simulated annealing (SA) algorithm. The computational results from the real case and a set of generated test problems show that GA-VNS provides a very good approximation of the MIP solutions at a much shorter computational time while outperforming the other compared algorithms. The analysis of the results reveals that it is beneficial to apply different transport vehicles rather than identical vehicles for SLTVSP., Published online: 17 Dec 2019

Minimizing the non-machining time of CNC machines requires optimal positioning of cutting tools on indexes (stations) of CNC machine turret magazine. This work presents a genetic algorithm with a novel solution representation and genetic operators to find the best possible index positions while tool duplicates and tools life are taken in to account during the process. The tool allocation in a machining process of a crankshaft with 10 cutting operations, on a 45-index magazine, is optimized for the entire life of the tools on the magazine. The tool-indexing time is considerably reduced compared to the current index positions being used in an automotive factory.