Three metaheuristics for solving the flow shop problem with permutation and sequence dependent setup time

Abstract

We present in this paper, three metaheuristics for the resolution of the flow shop scheduling problem with permutation and sequence dependent setup time. The first metaheuristic is the iterative local search algorithm, the second is the greedy randomized adaptive search procedure and the third is the greedy iterative algorithm. The goal is to minimize the total running time of all jobs, the makespan. In the three metaheuristics, during the improvement phase, we suggest a set of local research methods that we adopt for the studied problem. A comparative study is conducted on a set of instances by varying the parameters for each metaheuristic. The results obtained show good performances of the iterative greedy algorithm compared to two other metaheuristics.