A tutorial survey of job-shop scheduling problems using genetic algorithms—I. representation

Abstract Job-shop scheduling problem (abbreviated to JSP) is one of the well-known hardest combinatorial optimization problems. During the last three decades, the problem has captured the interest of a significant number of researchers and a lot of literature has been published, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. This has led to recent interest in using genetic algorithms (GAs) to address it. The purpose of this paper and its companion (Part II: Hybrid Genetic Search Strategies) is to give a tutorial survey of recent works on solving classical JSP using genetic algorithms. In Part I, we devote our attention to the representation schemes proposed for JSP. In Part II, we will discuss various hybrid approaches of genetic algorithms and conventional heuristics. The research works on GA/JSP provide very rich experiences for the constrained combinatorial optimization problems. All of the techniques developed for JSP may be useful for other scheduling problems in modern flexible manufacturing systems and other combinatorial optimization problems.