Multiobjective flow shop deteriorating scheduling problem via an adaptive multipopulation genetic algorithm

Abstract

Recently, the ow shop scheduling problem with deteriorating jobs has gained increasing concern from academic communities and industrial areas. The de- teriorating job is that the processing time of the jobs depends on their starting time. Due to the essential complexity of this problem, most studies focus on the two and three stages setting, and there are few studies that have considered a multiple stage setting. In this paper, a multiobjective ow shop deteriorating scheduling problem is considered, where the objectives are to minimize the makespan and the total tardiness, simultaneously. In order to solve it e_ciently, a novel adaptive multipopulation mul- tiobjective genetic algorithm is proposed. In the proposed algorithm, multiple scalar optimization problems of the multiobjective ow shop deteriorating scheduling prob-lem are developed, which will be introduced into the iteration course in multiple stages. An adaptive multipopulation strategy is designed to construct multiple subpopulations to search the optimal solutions of several scalar optimization problems in parallel. In addition, some special strategies, i.e. selection, crossover, mutation and the evolution of external archives are designed to improve the performance of the adaptive multi-population multiobjective genetic algorithm for the investigated multiobjective ow shop deteriorating scheduling problem. Based on a set of test instances on the multiob-jective ow shop deteriorating scheduling problem, simulation experiments are carried out to examine the performance of adaptive multipopulation multiobjective genetic al-gorithm in comparison with several peer multiobjective evolutionary algorithms. The experimental results show that the proposed adaptive multipopulation multiobjective genetic algorithm performs well when solving the multiobjective ow shop deteriorating scheduling problem.