Optimizing Production Scheduling in Smart Manufacturing Systems Using Hybrid Simulation-Based Multi-Objective Optimization
DOI:
https://doi.org/10.30743/kgar4363Keywords:
Management; Smart Manufacturing; Production; Scheduling; Simulation; OptimizationAbstract
Abstract is In the era of Industry 4.0, optimizing production operations under dynamic and uncertain environments has become a critical challenge. This study presents a hybrid optimization framework combining discrete-event simulation (DES) and a multi-objective metaheuristic algorithm to enhance production scheduling in smart manufacturing systems. The proposed model addresses trade-offs between throughput, energy consumption, and machine utilization, enabling real-time adaptive decision-making. Experiments were conducted on a flexible job shop scenario, with results indicating significant improvements in operational efficiency compared to conventional heuristics. The research highlights the potential of integrating simulation-based optimization for robust and sustainable production operations.
References
. Zhang, Y., Wang, L., & Liu, X. (2022). Hybrid simulation-based optimization for job-shop scheduling under uncertainty. International Journal of Production Research, 60(11), 3274–3290.
. Lee, C., & Kim, J. (2023). Sustainable production scheduling using multi-objective optimization in smart factories. Journal of Cleaner Production, 395, 136521.
. Baki, M. F., & Chan, W. (2021). Simulation and optimization integration in manufacturing systems: A review. Simulation Modelling Practice and Theory, 108, 102304.
. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
. Heilala, J., et al. (2020). Energy-efficiency indicators for production systems. Procedia CIRP, 81, 1422–1427.
. Fang, K., Liu, J., & Wang, Q. (2021). Integrated optimization of production scheduling and energy consumption using multi-objective algorithms. Energy, 235, 121263.
. Li, Y., & Wang, S. (2020). Real-time job shop scheduling based on reinforcement learning. Journal of Manufacturing Systems, 56, 271–283.
. Chen, X., Zhang, Y., & Zhao, L. (2021). Cloud-based manufacturing scheduling using simulation-optimization approaches. Robotics and Computer-Integrated Manufacturing, 68, 102059.
. Pan, Y., & Nagi, R. (2022). Sustainable operations scheduling: A review and framework. Computers & Industrial Engineering, 168, 108037.
. Sarker, B. R., & Newton, C. (2023). Metaheuristics in production scheduling: Trends and future directions. Operations Research Perspectives, 10, 100245.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Derlini, Selly Annisa, Zulkarnain Lubis
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
