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

• Published in: Journal of manufacturing systems 2024-02, Vol.72, p.287-307
• Main Authors: Mahmoodi, Ehsan, Fathi, Masood, Tavana, Madjid, Ghobakhloo, Morteza, Ng, Amos H.C.
• Format: Article
• Language: English
• Subjects: Data-driven simulation; Decision support system; Engineering Science with specialization in industrial engineering and management; High-mix low-volume; Industry 4.0; Meta-learning; Multi-objective optimization; Resource allocation; Teknisk fysik med inriktning mot industriell teknik; Virtual Production Development (VPD)
• ISSN: 0278-6125; 1878-6642; 1878-6642
• DOI: 10.1016/j.jmsy.2023.11.019

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. •Propose a DSS for resource allocation in smart manufacturing systems.•Use Simulation-based bottleneck analysis and multi-objective optimization.•Improve NSGA-II using a meta-learning mechanism.•Use clustering-based post-optimality analysis to derive actionable insights.•Demonstrate efficacy through a real-life marine engine manufacturing study.