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Data-driven simulat...
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Mahmoodi, EhsanHögskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Virtual Production Development (VPD)
(författare)
Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
- Artikel/kapitelEngelska2024
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Elsevier,2024
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LIBRIS-ID:oai:DiVA.org:his-23465
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https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23465URI
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https://doi.org/10.1016/j.jmsy.2023.11.019DOI
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-518023URI
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Språk:engelska
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Sammanfattning på:engelska
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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.
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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.
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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
(författare)
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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
(författare)
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Ghobakhloo, MortezaUppsala universitet,Industriell teknik(Swepub:uu)morgh451
(författare)
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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
(författare)
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Högskolan i SkövdeInstitutionen för ingenjörsvetenskap
(creator_code:org_t)
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Ingår i:Journal of manufacturing systems: Elsevier72, s. 287-3070278-61251878-6642
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