Using genetic algorithm for job-shop scheduling problems with reentrant product flows

We describe a job-shop scheduling method using a genetic algorithm for a production system with reentrant product flows. Fundamentally, the scheduling problem is a sequencing problem of operating order for lots on each process or machine. The difficulty in job-shop scheduling problems with reentrant product flows are these two points. The first point is that there are a large number of processes in spite of several process types. The second point is a complex material flow. The problem which we consider is that order restrictions with operating sequences are complicated and enormous. To cope with these problems, we propose coding and decoding methods which include order restrictions easily. To examine the performance of the proposed methods, numerical examples are presented