Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making





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.


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How to Cite

Mzili, T., Mzili, I., & Riffi , M. E. (2023). Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making. Decision Making: Applications in Management and Engineering, 6(2), 16–42. https://doi.org/10.31181/dmame060123042023m