A Non-dominated Genetic Algorithm Based on Decoding Rule of Heat Treatment Equipment Volume and Job Delivery Date

This paper investigated the flexible job-shop scheduling problem with heat treatment process. To solve this problem, we built an unified mathematical model of process and machining Up to now, has not been much. Based on features are intended minimize Cmax, maximize space utilization rate equipment, total delay penalty optimize scheduling. By taking dynamic arrival under consideration, proposed a set decoding rules based equipment volume job delivery date achieve hybrid solution during one procedure. When is maximized, taken it preferred number workpiece batches in same job, reduce waiting time pending job. In combination improved adaptive non-dominated genetic algorithm, worked out solution. Furthermore, verified effectiveness algorithm through comparison calculation results. Finally, software system for verification was developed verify validity our algorithm.