OPTIMIZATION MULTI-OBJECTIVE PARALLEL MULTI-WORK SHOP FLOW-SHOP SCHEDULING PROBLEM UNDER NO-WAITING CONSTRAINT

ABSTRACT: Currently, a novel generalization of the flow-shop scheduling issue within a multi-workshop setting is raising increasing apprehension among the operational research community. In this article, we concentrate on a system seldom seen in the literature, which is made up of three identical workshops installed in parallel. A specific issue of scheduling in parallel flow-shop multi-workshop environments with no waiting constraint (No-Wait PFSSP) is examined. Two combined objective functions need to be minimized: the just-in-time (the sum of the averages of advance and delay) and the makespan. The drawback commonly faced in this kind of workshop is the uneven distribution of workloads among various workshops. Consequently, a load balancing phase among the various workshops was incorporated into the optimization procedures. For the analysis of this issue, three optimization meta-heuristics are proposed: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). To assess the significance of the results obtained and the effectiveness of the proposed algorithms, numerous tests have been conducted. The findings indicated that PSO is the most effective algorithm, demonstrating high success rates, consistent performance, and robust optimization, thus making it ideal for real-world applications in time-sensitive sectors. GA obtained moderate outcomes, particularly in minor cases. It demanded additional computation time, which restricted efficacy in bigger scenarios. While quick and effective regarding CPU usage, the algorithm (PSO) exhibits greater variability and diminished reliability.