On a bi-objective distributed permutation flow shop problem with batch processing machines

This paper is devoted to a bi-objective distributed permutation flow shop problem with batch processing machines. We provide an industrial example illustrating this flow shop optimization issue that may be encountered in the textile sector, with particular focus on the process of T-shirt production. The tackled bi-objective function combines the maximum completion time (makespan) and the maximum tardiness. A weighting coefficient will be associated to each criterion in order to favor one or the other. The sequence independent setup time will be assumed in order to take into account the preparation of machines to process the different batches. Three metaheuristics have been used to address this problem. The first one is the iterated local search (ILS) metaheuristic, followed by the iterated greedy (IG) and finally the genetic algorithm (GA). The numerical simulations indicate that the developed IG metaheuristic performs better in terms of comparison to the other two algorithms ILS and GA for different size instances.