A simulated annealing algorithm for job shop scheduling

Abstract

In this paper, the job shop scheduling problem is considered with the objective of minimization of makespan time. We first reviewed the literature on job shop scheduling using meta-heuristics. Then a simulated annealing algorithm is presented for scheduling in a job shop. To create neighbourhoods, three perturbation schemes, viz. pairwise exchange, insertion, and random insertion are used, and the effect of them on the final schedule is also compared. The proposed simulated annealing algorithm is compared with existing genetic algorithms and the comparative results are presented. For comparative evaluation, a wide variety of data sets are used. The proposed algorithm is found to perform well for scheduling in the job shop.