Abstract
This paper investigates a multi-objective optimization distributed no-wait permutation flow shop scheduling problem under the constraint of sequence dependent setup time. Our optimization case is minimizing the makespan and maximum tardiness criteria. Therefore, our main objective will be to find the optimal jobs sequence that minimizes a function representing the two criteria of makespan and maximum tardiness. This function will be linearly dependent of these two criteria via a weighting parameter for each criterion. To solve this industrial problem, we propose the mixed integer linear programming (MILP) and a set of efficient metaheuristics solving different size instances. To this end, we suggest three inspired nature metaheuristics: The genetic algorithm (GA), the artificial bee colony (ABC) algorithm and migratory bird optimization (MBO) algorithm. We suggest a total of six new algorithms based on nature-inspired metaheuristics. Also, two constructive heuristics are used, the greedy randomized adaptive search procedure (GRASP) and Nawaz–Enscore–Ham (NEH) algorithms. It was revealed that GA algorithm with NEH initialization gives the best results comparing to the other metaheuristics.
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