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Data-driven simulat...
Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
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- Mahmoodi, Ehsan (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Virtual Production Development (VPD)
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- 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
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- 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
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- Ghobakhloo, Morteza (author)
- Uppsala universitet,Industriell teknik
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- 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.
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In: Journal of manufacturing systems. - : Elsevier. - 0278-6125 .- 1878-6642. ; 72, s. 287-307
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Abstract
Subject headings
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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.
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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