Effective Neighborhood Generation Method in Search Algorithm for Flexible Job Shop Scheduling Problem

Abstract

The flexible job shop scheduling problem (FJSSP) is an extension of the classical job shop scheduling problem (JSSP) that allocates jobs to resources while minimizing the maximum completion time of all the jobs. Machine assignment and job sequence are determined in the FJSSP. To efficiently solve the FJSSP, which is a non-deterministic polynomial-time hard problem, a heuristic method must be used. In previous studies, the FJSSP has been solved using neighborhood algorithms that employ various metaheuristic methods. These approaches constrain the neighborhood operation to jobs on a critical path and simultaneously change the machine assignment and job sequence. Branches on the critical path are easily generated in the FJSSP search processes; this branch structure can improve the efficiency of the FJSSP. This study investigates two neighborhood search algorithms used for changing the machine assignment and job sequence via a critical path. The first method changes the machine assignment and job sequence simultaneously, whereas the second method changes them independently. In this study, we propose an efficient neighborhood generating method using a branch block of critical path.