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- Mahmoodi, EhsanHögskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Virtual Production Development (VPD) (author)
Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
- Article/chapterEnglish2024
Publisher, publication year, extent ...
- Elsevier,2024
- electronicrdacarrier
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- LIBRIS-ID:oai:DiVA.org:his-23465
- https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23465URI
- https://doi.org/10.1016/j.jmsy.2023.11.019DOI
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-518023URI
Supplementary language notes
- Language:English
- Summary in:English
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- CC BY 4.0 DEEDCorresponding author at: Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden. E-mail address: masood.fathi@his.se (M. Fathi).This study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency via the ACCURATE 4.0 (grant agreement No. 20200181) and PREFER projects, respectively.
- 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.
Subject headings and genre
- TEKNIK OCH TEKNOLOGIER Maskinteknik Produktionsteknik, arbetsvetenskap och ergonomi hsv//swe
- ENGINEERING AND TECHNOLOGY Mechanical Engineering Production Engineering, Human Work Science and Ergonomics hsv//eng
- TEKNIK OCH TEKNOLOGIER Annan teknik hsv//swe
- ENGINEERING AND TECHNOLOGY Other Engineering and Technologies hsv//eng
- Resource allocation
- High-mix low-volume
- Multi-objective optimization
- Data-driven simulation
- Decision support system
- Industry 4.0
- Meta-learning
- Virtual Production Development (VPD)
- Virtual Production Development (VPD)
- Engineering Science with specialization in industrial engineering and management
Added entries (persons, corporate bodies, meetings, titles ...)
- Fathi, MasoodUppsala universitet,Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Division of Industrial Engineering and Management, Uppsala University, Sweden,Virtual Production Development (VPD),Industriell teknik,Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden(Swepub:uu)masfa660 (author)
- Tavana, MadjidBusiness Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, USA ; Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, Germany (author)
- Ghobakhloo, MortezaUppsala universitet,Industriell teknik(Swepub:uu)morgh451 (author)
- Ng, Amos H. C.,1970-Uppsala universitet,Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Division of Industrial Engineering and Management, Uppsala University, Sweden,Virtual Production Development (VPD),Industriell teknik,Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden(Swepub:uu)among495 (author)
- Högskolan i SkövdeInstitutionen för ingenjörsvetenskap (creator_code:org_t)
Related titles
- In:Journal of manufacturing systems: Elsevier72, s. 287-3070278-61251878-6642
Internet link
- https://doi.org/10.1016/j.jmsy.2023.11.019Fulltext
- https://his.diva-portal.org/smash/get/diva2:1819070/FULLTEXT01.pdffulltext:print
- https://uu.diva-portal.org/smash/get/diva2:1819961/FULLTEXT01.pdffulltext:print
- https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23465
- https://doi.org/10.1016/j.jmsy.2023.11.019
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-518023
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