Genetic algorithm approach to job shop scheduling and its use in real-timecases

In this paper we present a GA (Genetic Algorithm) approach combined with th e concept of GT (Group Technology) to solve the job shop scheduling problem s. The main idea is to organize all these jobs into groups using GT and sol ve such a group scheduling problem with GA. Due to the similarities between jobs within a group, scheduling (a group) can be treated easily as if it i s a flow shop problem. Sin ce the complexity of the problem has been simpli fied, the time spent in finding a feasible scheduling of the whole problem can be decreased. Ideas of using GA in real-time cases are discussed and ex plored. In particular, the concept of using a near-optimal evolution genera tion n* in GA is introduced. The value of n* is related to the desired perf ormance index, and using n* in GA may ensure more effective searching. An i llustrative example is given at the end of the paper.