Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making
Keywords:Artificial Rat Swarm Optimization, flow shop problem, scheduling, manufacturing systems, machine processing, job sequence, optimization, metaheuristic algorithms, solution quality, computational efficiency
The Rat Swarm Optimizer (RSO) algorithm is examined in this paper as a potential remedy for the flow shop issue in manufacturing systems. The flow shop problem involves allocating jobs to different machines or workstations in a certain order to reduce execution time or resource use. The objective function is used by the RSO method to optimize the results after mapping the rat locations to task-processing sequences. The RSO method successfully locates high-quality solutions to the flow shop problem when compared to other metaheuristic algorithms on diverse test situations. This research helps to improve the flexibility, lead times, quality, and efficiency of the production system. The paper introduces the RSO algorithm, creates a mapping strategy, redefines mathematical operators, suggests a method to enhance the quality of solutions, shows how successful the algorithm is through simulations and comparisons, and then uses statistical analysis to confirm the algorithm's performance.
Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A Comprehensive Review of Swarm Optimization Algorithms. PLOS ONE, 10(5), e0122827. https://doi.org/10.1371/journal.pone.0122827
Alaliyat, S., Yndestad, H., & Sanfilippo, F. (2014). Optimisation Of Boids Swarm Model Based On Genetic Algorithm And Particle Swarm Optimisation Algorithm (Comparative Study). ECMS 2014 Proceedings Edited by: Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani, 643–650. https://doi.org/10.7148/2014-0643
Babaee Tirkolaee, E., Goli, A., & Weber, G.-W. (2020). Fuzzy Mathematical Programming and Self-Adaptive Artificial Fish Swarm Algorithm for Just-in-Time Energy-Aware Flow Shop Scheduling Problem With Outsourcing Option. IEEE Transactions on Fuzzy Systems, 28(11), 2772–2783. https://doi.org/10.1109/TFUZZ.2020.2998174
Bellabai, J. R., Leela, B. N. M., & Kennedy, S. M. R. (2022). Testing the Performance of Bat-Algorithm for Permutation Flow Shop Scheduling Problems with Makespan Minimization. Brazilian Archives of Biology and Technology, 65. https://doi.org/10.1590/1678-4324-2022210840
Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4), 353–373.
Ding, J.-Y., Song, S., Gupta, J. N. D., Zhang, R., Chiong, R., & Wu, C. (2015). An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Applied Soft Computing, 30, 604–613.
Huang, Y.-M., & Lin, J.-C. (2011). A new bee colony optimization algorithm with idle-time-based filtering scheme for open shop-scheduling problems. Expert Systems with Applications, 38(5), 5438–5447.
Kurdi, M. (2021). Application of Social Spider Optimization for Permutation Flow Shop Scheduling Problem. Journal of Soft Computing and Artificial Intelligence, 2(2), 85–97.
Liang, Z., Zhong, P., Liu, M., Zhang, C., & Zhang, Z. (2022). A computational efficient optimization of flow shop scheduling problems. Scientific Reports, 12(1), 845. https://doi.org/10.1038/s41598-022-04887-8
Li, X., & Yin, M. (2013). A hybrid cuckoo search via Lévy flights for the permutation flow shop scheduling problem. International Journal of Production Research, 51(16), 4732–4754.
Marichelvam, M. K., Tosun, Ö., & Geetha, M. (2017). Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time. Applied Soft Computing, 55, 82–92. https://doi.org/10.1016/j.asoc.2017.02.003
Mzili, T., Riffi, M. E., Mzili, I., & Dhiman, G. (2022). A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem. Decision Making: Applications in Management and Engineering, 5(2), 287–299.
Pan, Q.-K., Wang, L., & Zhao, B.-H. (2008). An improved iterated greedy algorithm for the no-wait flow shop scheduling problem with makespan criterion. The International Journal of Advanced Manufacturing Technology, 38(7–8), 778–786.
Reza Hejazi *, S., & Saghafian, S. (2005). Flowshop-scheduling problems with makespan criterion: a review. International Journal of Production Research, 43(14), 2895–2929.
Smutnicki, C., Pempera, J., Bocewicz, G., & Banaszak, Z. (2022). Cyclic flow-shop scheduling with no-wait constraints and missing operations. European Journal of Operational Research, 302(1), 39–49.
Tanaev, V. S., Gordon, V. S., & Shafransky, Y. M. (1994). NP-Hard Problems. In Scheduling Theory. Single-Stage Systems (pp. 253–311). Springer Netherlands. https://doi.org/10.1007/978-94-011-1190-4_5
Wang, J., & Magron, V. (2022). Exploiting Sparsity in Complex Polynomial Optimization. Journal of Optimization Theory and Applications, 192(1), 335–359.
Wu, X., Yan, X., & Wang, L. (2022). Optimizing job release and scheduling jointly in a reentrant hybrid flow shop. Expert Systems with Applications, 209, 118278. https://doi.org/10.1016/j.eswa.2022.118278
Zhang, J., Zhang, C., & Liang, S. (2010). The circular discrete particle swarm optimization algorithm for flow shop scheduling problem. Expert Systems with Applications, 37(8), 5827–5834.
Zheng, C., Xing, J., Wang, Z., Qin, X., Eynard, B., Li, J., Bai, J., & Zhang, Y. (2022). Knowledge-based program generation approach for robotic manufacturing systems. Robotics and Computer-Integrated Manufacturing, 73, 102242. https://doi.org/10.1016/j.rcim.2021.102242
How to Cite
Copyright (c) 2023 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.