Scheduling hybrid flowshop with sequence-dependent setup times and due windows to minimize total weighted earliness and tardiness

Abstract

Abstract This paper studies scheduling of hybrid flowshop with sequence-dependent setup times to minimize total weighted earliness and tardiness. Due dates are usually intervals in real life therefore due windows are considered in this paper. Since the considered problem is NP-hard, three population based hybrid metaheuristics are presented, namely: Hybrid squirrel search algorithm (HSSA), opposition based whale optimization algorithm (OBWOA), and discrete grey wolf optimization (DGWO). We utilized some effective and advanced technologies, including variable neighbourhood search, hybrid local search and opposition based learning to enhance the performance of the presented algorithms. The parameters of the presented algorithms are calibrated using design of experiments approach. To evaluate the performance of the presented algorithms, we compare them against five other well-known meta-heuristics on 240 randomly generated problems. The statistically sound results demonstrate that the presented algorithms are very competitive for the considered problem.