Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing

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Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing

Mahmoodi, Ehsan (author): Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Virtual Production Development (VPD)

Fathi, Masood (author): 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

Tavana, Madjid (author): Business 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

Ghobakhloo, Morteza (author): Uppsala universitet,Industriell teknik

Ng, Amos H. C., 1970- (author): 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

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Elsevier, 2024
2024
English.
In: Journal of manufacturing systems. - : Elsevier. - 0278-6125 .- 1878-6642. ; 72, s. 287-307

Related links:: https://doi.org/10.1...; show more...; https://his.diva-por... (primary) (Raw object); https://uu.diva-port... (primary) (Raw object); https://urn.kb.se/re...; https://doi.org/10.1...; https://urn.kb.se/re...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

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

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)

Keyword

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

Publication and Content Type

ref (subject category)
art (subject category)

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By the author/editor: Mahmoodi, Ehsan; Fathi, Masood; Tavana, Madjid; Ghobakhloo, Mort ...; Ng, Amos H. C., ...

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