Due window scheduling with sequence-dependent setup on parallel machines using three hybrid metaheuristic algorithms

Due-date determination problems have gained significant attention in recent years due to the industrial focus in the just-in-time philosophy. This paper considers a machine scheduling problem where jobs should be completed at times as close as possible to their respective due dates, and hence, both earliness and tardiness should be penalized. It is assumed that earliness and tardiness (ET) penalties will not occur if a job is completed within the due window. However, ET penalties will occur if a job is completed outside the due window. The objective is to determine a schedule that minimizes sum of the earliness and tardiness of jobs. To achieve this objective, three hybrid metaheuristics are proposed. The first metaheuristic is a hybrid algorithm which combines elements from both simulated annealing (SA) as constructive heuristic search and a variable neighborhood search (VNS) as local search improvement technique. The second one presents a hybrid metaheuristic algorithm which composed of a population generation method based on an ant colony optimization (ACO) and a VNS to improve the population. Finally, a hybrid metaheuristic approach is proposed which integrates several features from ACO, SA, and VNS in a new configurable scheduling algorithm. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. Computational experiments conducting on 252 randomly generated problems compare the results with the VNS algorithm proposed previously and show that the procedure is capable of producing consistently good results.