Solve job shop scheduling problem with sequence dependent setup times using a local search algorithm with different neighborhood search structure

Abstract

In this study examines job shop scheduling problems with sequence dependent setup times under objective function minimization of makespan (JSSP/SDST/ Cmax ). An effective meta-heuristic, local search is a meta-heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution maximizing or minimizing a criterion among a number of candidate solutions. Local search algorithms move from solution to solution in the space of candidate solutions (the search space) by applying local changes, until a solution deemed optimal is found or a time bound is elapsed. The performance of the local search depends on its neighborhood search structure (NSS). We used five methods from neighborhood search: Swap, Migration Mechanism (MM), Inversion, shift, and a proposed robust neighborhood search method. The results showed that the new PNS method gives less makespan value with different problems size (15x15, 20x15, 20x20, 30x15, 30x20, 50x15, 50x20 and 100x20) taken from the OR- library compared to previous well known neighborhood search methods.