A Study on Efficient Method of Job-Shop Scheduling Based on Simulated Annealing

Abstract

Diversification of customers’ needs has caused transition from low-mix high volume manufacturing to high-mix low-volume manufacturing. This transition has increased importance of job-shop scheduling. Because job-shop scheduling problem (JSP) is non-deterministic polynomial time hard, approximate optimization based on meta-heuristics have been actively discussed, and methods using simulated annealing (SA) have been proposed. SA has a disadvantage that good solutions cannot be obtained efficiently if the initial solution is not given appropriately. Methods for solving this problem have been proposed for JSPs aiming at minimizing makespan. In high-mix low-volume manufacturing, it is important to minimize average lead time in addition to makespan. For this reason, this research deals with development of an efficient method using SA for JSPs aiming at minimizing average lead time. A method of neighborhood limitation for reducing average lead time was developed by focusing on waiting time of operations, and an improved SA in which the neighborhood limitation method is used with a given probability was proposed. Effectiveness of the proposed method was shown by numerical examples.