A computational study of neighborhood operators for job-shop scheduling problems with regular objectives

Abstract

Job-shop scheduling problems have received a considerable attention in the literature. While the most tackled objective in this area is makespan, job-shop scheduling problems with other objectives such as the minimization of the weighted or unweighted tardiness, the number of late jobs, or the sum of the jobs’ completion times have been considered. However, the problems under the latter objectives have been generally less studied than makespan. In this paper, we study job-shop scheduling under various objectives. In particular, we examine the impact various neighborhood operators have on the performance of iterative improvement algorithms, the composition of variable neighborhood descent algorithms, and the performance of metaheuristics such as iterated local search in dependence of the type of local search algorithm used.