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Barrera Diaz, C. A., Nourmohammadi, A., Smedberg, H., Aslam, T. & Ng, A. H. C. (2023). An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems. Mathematics, 11(6), Article ID 1527.
Open this publication in new window or tab >>An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems
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2023 (English)In: Mathematics, ISSN 2227-7390, Vol. 11, no 6, article id 1527Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
reconfigurable manufacturing system, simulation, multi-objective optimization, knowledge discovery
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Virtual Production Development (VPD); VF-KDO
Identifiers
urn:nbn:se:his:diva-22329 (URN)10.3390/math11061527 (DOI)000960178700001 ()2-s2.0-85151391170 (Scopus ID)
Funder
Knowledge Foundation, 2018-0011
Note

CC BY 4.0

(This article belongs to the Special Issue Multi-Objective Optimization and Decision Support Systems)

Received: 15 February 2023 / Revised: 15 March 2023 / Accepted: 17 March 2023 / Published: 21 March 2023

The authors thank the Knowledge Foundation, Sweden (KKS) for funding this research through the KKS Profile Virtual Factories with Knowledge-Driven Optimization, VF-KDO, grant number 2018-0011.

Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2023-09-01Bibliographically approved
Flores-García, E., Barrera Diaz, C. A., Wiktorsson, M., Ng, A. H. C. & Aslam, T. (2023). Enabling CPS and simulation-based multi-objective optimisation for material handling of reconfigurable manufacturing systems. The International Journal of Advanced Manufacturing Technology
Open this publication in new window or tab >>Enabling CPS and simulation-based multi-objective optimisation for material handling of reconfigurable manufacturing systems
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2023 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Other academic) Submitted
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23114 (URN)
Funder
Vinnova
Note

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.

Available from: 2023-08-18 Created: 2023-08-18 Last updated: 2023-09-11Bibliographically approved
Lidberg, S., Frantzén, M., Aslam, T. & Ng, A. H. C. (2022). A Knowledge Extraction Platform for Reproducible Decision-Support from Multi-Objective Optimization Data. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 725-736). Amsterdam; Berlin; Washington, DC: IOS Press
Open this publication in new window or tab >>A Knowledge Extraction Platform for Reproducible Decision-Support from Multi-Objective Optimization Data
2022 (English)In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 725-736Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam; Berlin; Washington, DC: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
multi-objective optimization, knowledge extraction, industry 4.0, decision-support, industrial optimization
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21115 (URN)10.3233/ATDE220191 (DOI)2-s2.0-85132829202 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022
Note

CC BY-NC 4.0

Corresponding Author: Simon Lidberg, Högskolevägen, BOX 1231, Skövde, Sweden; E-mail: simon.lidberg@his.se

Available from: 2022-05-04 Created: 2022-05-04 Last updated: 2023-02-22Bibliographically approved
Aslam, T., Goienetxea Uriarte, A. & Svensson, H. (2022). Education of the Future: Learnings and Experiences from Offering Education to Industry Professionals. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 665-676). Amsterdam; Berlin; Washington, DC: IOS Press
Open this publication in new window or tab >>Education of the Future: Learnings and Experiences from Offering Education to Industry Professionals
2022 (English)In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 665-676Conference paper, Published paper (Refereed)
Abstract [en]

Digitalization is forcing the industry to rethink current practices in all business domains, pushing for a digital transformation of business and operations at a high rate and, thus, paving the way for new business models and making others redundant. For small and medium-sized companies (SME), in particular, it is an enormous challenge to keep up with the pace of technological development. Several initiatives have argued the industry’s need for continuous digitalization, innovation, transformation ability, and future skills and competencies development. However, the advancement of the Swedish industry in this area has been uneven, where larger organizations have begun their digital transformation journey to some extent, but SMEs risk falling behind. In addition to the technological transformation, the challenges regarding the industries’ skills supply need to be solved, where a workforce with the right competencies, knowledge, and skill sets are equally, if not more, important for remaining competitive. One of the key elements to face these challenges in the companies will be to recruit knowledgeable employees or re-skill the existing ones. Efficient access to relevant knowledge and skills is still a major concern for companies that will surely affect their competitiveness for a long time to come. This paper elaborates on the opportunities and challenges that Swedish universities face in the context of lifelong learning and education for industry professionals. The paper presents results and experiences gained from a lifelong learning project for industry professionals at the University of Skövde in collaboration with ten industry partners. The results from the project show that in addition to pedagogical methods, current structures and policies within academia need to be further developed to effectively serve industry professionals. The paper also presents a concept of education for industry professionals in the lifelong learning context based on the results and experience gained from the project.

Place, publisher, year, edition, pages
Amsterdam; Berlin; Washington, DC: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
Education, Lifelong Learning, Digital Transformation, Industry professionals
National Category
Pedagogy Learning Information Systems, Social aspects Production Engineering, Human Work Science and Ergonomics Mechanical Engineering Robotics Control Engineering
Research subject
Production and Automation Engineering; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-21110 (URN)10.3233/ATDE220185 (DOI)2-s2.0-85132822763 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022
Projects
Virtual FactoryWISER
Funder
Knowledge Foundation
Note

CC BY-NC 4.0

Corresponding Author: tehseen.aslam@his.se

The authors gratefully acknowledge the Swedish Knowledge Foundation for funding the projects Virtual Factory and WISER as part of their Graduate Professional Development projects (Expertkompetens), which strengthen education through the development of flexible, research-linked courses at advanced level for working professionals.

Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2022-12-16Bibliographically approved
Igelmo, V., Syberfeldt, A., Hansson, J. & Aslam, T. (2022). Enabling Industrial Mixed Reality Using Digital Continuity: An Experiment Within Remanufacturing. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 497-507). Amsterdam; Berlin; Washington, DC: IOS Press
Open this publication in new window or tab >>Enabling Industrial Mixed Reality Using Digital Continuity: An Experiment Within Remanufacturing
2022 (English)In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 497-507Conference paper, Published paper (Refereed)
Abstract [en]

In the digitalisation era, overlaying digital, contextualised information on top of the physical world is essential for an efficient operation. Mixed reality (MR) is a technology designed for this purpose, and it is considered one of the critical drivers of Industry 4.0. This technology has proven to have multiple benefits in the manufacturing area, including improving flexibility, efficacy, and efficiency. Among the challenges that prevent the big-scale implementation of this technology, there is the authoring challenge, which we address by answering the following research questions: (1) “how can we fasten MR authoring in a manufacturing context?” and (2) “how can we reduce the deployment time of industrial MR experiences?”. This paper presents an experiment performed in collaboration with Volvo within the remanufacturing of truck engines. MR seems to be more valuable for remanufacturing than for many other applications in the manufacturing industry, and the authoring challenge appears to be accentuated. In this experiment, product lifecycle management (PLM) tools are used along with internet of things (IoT) platforms and MR devices. This joint system is designed to keep the information up-to-date and ready to be used when needed. Having all the necessary data cascading from the PLM platform to the MR device using IoT prevents information silos and improves the system’s overall reliability. Results from the experiment show how the interconnection of information systems can significantly reduce development and deployment time. Experiment findings include a considerable increment in the complexity of the overall IT system, the need for substantial investment in it, and the necessity of having highly qualified IT staff. The main contribution of this paper is a systematic approach to the design of industrial MR experiences.

Place, publisher, year, edition, pages
Amsterdam; Berlin; Washington, DC: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
Mixed reality, Digital Continuity, Product Lifecycle Management, Remanufacturing, Industry 4.0
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Other Mechanical Engineering Computer Systems
Research subject
Production and Automation Engineering; Distributed Real-Time Systems; VF-KDO
Identifiers
urn:nbn:se:his:diva-21105 (URN)10.3233/ATDE220168 (DOI)2-s2.0-85132823251 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022
Funder
Vinnova, 2019-00787
Note

CC BY-NC 4.0

Corresponding Author: victor.igelmo.garcia@his.se

The authors wish to thank the Swedish innovation agency Vinnova and the Strategic Innovation Programme Produktion2030 (funding number 2019-00787). Likewise, the authors [wish to thank Volvo AB.]

Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2023-02-22Bibliographically approved
Pehrsson, L., Aslam, T. & Frantzén, M. (2021). Aggregated models for decision-support in manufacturing systems management. International Journal of Manufacturing Research, 16(3), 217-240
Open this publication in new window or tab >>Aggregated models for decision-support in manufacturing systems management
2021 (English)In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 16, no 3, p. 217-240Article in journal (Refereed) Published
Abstract [en]

Many industrial challenges can be related to the setup of manufacturing plants and supply chains. While there are techniques available for discrete event simulation of production lines, the opportunities of applying such techniques on higher manufacturing network levels are not explored to the same extent. With established methods for optimisation of manufacturing lines showing proven potential in conceptual analysis and development of production lines, the application of such optimisation methods on higher level manufacturing networks is a subject for further exploration. In this paper, an extended aggregation technique for discrete event simulation of higher level manufacturing systems is discussed, proposed, tested, and verified with real-world problem statements as a proof of concept. The contribution of the new technique is to enable the application of DES models, with reasonable computational requirements, at higher level manufacturing networks. The proposed technique can be used to generate valuable decision information supporting conceptual systems development.

Place, publisher, year, edition, pages
InderScience Publishers, 2021
Keywords
aggregation, discrete event simulation, DES, optimisation, decision-support, manufacturing systems management
National Category
Computer Sciences
Research subject
VF-KDO; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-21820 (URN)10.1504/IJMR.2021.117926 (DOI)000849830400001 ()
Note

Pehrsson, Leif (corresponding author)

Available from: 2022-09-16 Created: 2022-09-16 Last updated: 2022-11-24Bibliographically approved
Barrera Diaz, C. A., Fathi, M., Aslam, T. & Ng, A. H. C. (2021). Optimizing reconfigurable manufacturing systems: A Simulation-based Multi-objective Optimization approach. Paper presented at 54th CIRP Conference on Manufacturing Systems 2021, CMS 2021, Patras, 22 September 2021 through 24 September 2021, Code 175290. Procedia CIRP, 104, 1837-1842
Open this publication in new window or tab >>Optimizing reconfigurable manufacturing systems: A Simulation-based Multi-objective Optimization approach
2021 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 104, p. 1837-1842Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Simulation-based Optimization, Manufacturing Systems, Reconfigurability, Multi-Objective
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
VF-KDO; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-20724 (URN)10.1016/j.procir.2021.11.310 (DOI)2-s2.0-85121606978 (Scopus ID)
Conference
54th CIRP Conference on Manufacturing Systems 2021, CMS 2021, Patras, 22 September 2021 through 24 September 2021, Code 175290
Funder
Knowledge Foundation
Note

CC BY-NC-ND 4.0

Corresponding author Tel.: +46-500-448-586. E-mail address: carlos.alberto.barrera.diaz@his.se

Edited by Dimitris Mourtzis

Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2023-08-18Bibliographically approved
Barrera Diaz, C. A., Aslam, T. & Ng, A. H. C. (2021). Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach. IEEE Access, 9, 144195-144210
Open this publication in new window or tab >>Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach
2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 144195-144210Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Multi-objective optimization, reconfigurable manufacturing systems, simulation-based optimization, genetic algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-20674 (URN)10.1109/ACCESS.2021.3122239 (DOI)000712563100001 ()2-s2.0-85118540679 (Scopus ID)
Projects
VF-KDO
Funder
Knowledge Foundation
Note

CC BY 4.0

This 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). 

Available from: 2021-10-29 Created: 2021-10-29 Last updated: 2023-08-18Bibliographically approved
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2020). A hybrid simulation-based optimization framework supporting strategic maintenance to improve production performance. European Journal of Operational Research, 281(2), 402-414
Open this publication in new window or tab >>A hybrid simulation-based optimization framework supporting strategic maintenance to improve production performance
2020 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 281, no 2, p. 402-414Article in journal (Refereed) Published
Abstract [en]

Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research.

Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement.

Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Problem structuring, Decision support, System dynamics, Multi-objective optimization, Discrete-event simulation
National Category
Production Engineering, Human Work Science and Ergonomics Reliability and Maintenance
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-15064 (URN)10.1016/j.ejor.2019.08.036 (DOI)000497593000012 ()2-s2.0-85071569509 (Scopus ID)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2023-03-02Bibliographically approved
Lidberg, S., Aslam, T. & Ng, A. H. C. (2020). Multi-Level Optimization with Aggregated Discrete-Event Models. In: K.-H. Bae; B. Feng; S. Kim; S. Lazarova-Molnar; Z. Zheng; T. Roeder; R. Thiesing (Ed.), Proceedings of the 2020 Winter Simulation Conference: . Paper presented at Winter Simulation Conference, December 14-18, 2020, Virtual Conference (pp. 1515-1526). IEEE
Open this publication in new window or tab >>Multi-Level Optimization with Aggregated Discrete-Event Models
2020 (English)In: Proceedings of the 2020 Winter Simulation Conference / [ed] K.-H. Bae; B. Feng; S. Kim; S. Lazarova-Molnar; Z. Zheng; T. Roeder; R. Thiesing, IEEE, 2020, p. 1515-1526Conference paper, Published paper (Refereed)
Abstract [en]

Removing bottlenecks that restrain the overall performance of a factory can give companies a competitive edge. Although in principle, it is possible to connect multiple detailed discrete-event simulation models to form a complete factory model, it could be too computationally expensive, especially if the connected models are used for simulation-based optimizations. Observing that computational speed of running a simulation model can be significantly reduced by aggregating multiple line-level models into an aggregated factory level, this paper investigates, with some loss of detail, if the identified bottleneck information from an aggregated factory model, in terms of which parameters to improve, would be useful and accurate enough when compared to the bottleneck information obtained with some detailed connected line-level models. The results from a real-world, multi-level industrial application study have demonstrated the feasibility of this approach, showing that the aggregation method can represent the underlying detailed line-level model for bottleneck analysis.

Place, publisher, year, edition, pages
IEEE, 2020
Series
Proceedings of the Winter Simulation Conference, ISSN 0891-7736, E-ISSN 1558-4305
Keywords
Aggregation, Data mining, Decision support, Discrete event simulation, Industrial case study, Multi-objective optimization, Agglomeration, Decision making, Decision support systems, Decision trees, Digital storage, Multiobjective optimization, Trees (mathematics), Decision supports, Decision-tree algorithm, Industrial problem, Industrial systems, Knowledge extraction, Real-world optimization, Simulation-based optimizations
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
VF-KDO; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-19643 (URN)10.1109/WSC48552.2020.9383990 (DOI)000679196301044 ()2-s2.0-85103904651 (Scopus ID)978-1-7281-9499-8 (ISBN)978-1-7281-9500-1 (ISBN)
Conference
Winter Simulation Conference, December 14-18, 2020, Virtual Conference
Funder
Knowledge Foundation
Note

Copyright © 2020, IEEE

Available from: 2021-04-20 Created: 2021-04-20 Last updated: 2021-10-06Bibliographically approved
Projects
Next Generation Propulsion Production [2018-00426_Vinnova]; University of SkövdeVirtual factories with knowledge-driven optimization (VF-KDO); University of Skövde; Publications
Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2024). Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration. Computers & industrial engineering, 187, Article ID 109775. Lind, A., Iriondo Pascual, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2024). Multi-objective optimisation of a logistics area in the context of factory layout planning. Production & Manufacturing Research, 12(1), Article ID 2323484. Barrera Diaz, C. A., Nourmohammadi, A., Smedberg, H., Aslam, T. & Ng, A. H. C. (2023). An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems. Mathematics, 11(6), Article ID 1527. Lind, A., Hanson, L., Högberg, D., Lämkull, D., Mårtensson, P. & Syberfeldt, A. (2023). Digital support for rules and regulations when planning and designing factory layouts. Procedia CIRP, 120, 1445-1450Redondo Verdú, C., Sempere Maciá, N., Strand, M., Holm, M., Schmidt, B. & Olsson, J. (2023). Enhancing Manual Assembly Training using Mixed Reality and Virtual Sensors. Paper presented at 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '23, Gulf of Naples, Italy, 12 - 14 July 2023. Procedia CIRPLind, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2023). Extending and demonstrating an engineering communication framework utilising the digital twin concept in a context of factory layouts. International Journal of Services Operations and Informatics, 12(3), 201-224Danielsson, O., Syberfeldt, A., Holm, M. & Thorvald, P. (2023). Integration of Augmented Reality Smart Glasses as Assembly Support: A Framework Implementation in a Quick Evaluation Tool. International Journal of Manufacturing Research, 18(2), 144-164Smedberg, H. & Bandaru, S. (2023). Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization. European Journal of Operational Research, 306(3), 1311-1329Smedberg, H. (2023). Knowledge discovery for interactive decision support and knowledge-driven optimization. (Doctoral dissertation). Skövde: University of SkövdeSmedberg, H., Bandaru, S., Riveiro, M. & Ng, A. H. C. (2023). Mimer: A web-based tool for knowledge discovery in multi-criteria decision support. IEEE Computational Intelligence Magazine
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0880-2572

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