TABU SEARCH STRATEGIES FOR SOLVING JOB SHOP SCHEDULING PROBLEMS

Abstract

Scheduling is considered to be a major task to improve shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the global optimization technique called tabu search to the job shop scheduling problem. An easily implementable algorithm for the problem of finding a minimum makespan in a job shop is presented. The method employs critical paths and blocks of operations. The algorithm is based on a specific neighborhood and dynamic tabu length strategies. A neighboring solution is a solution obtained by permuting two successive and critical operations that use the same machine. Tabus are useful to help the search move away from previously visited portions of the search space and thus perform more extensive exploration. Experiments using well-known bench mark problems are carried out to check the performance of the proposed method.