Abstract: In part processing, due to various processing features, different processes, and process-constrained processing sequencing rules, there are large number of processes and machine choices. Thus, it is known that flexible process planning belongs to NP-hard problems. By using segment coding for process and machine selection, a constrained adjustment algorithm is designed to solve the process-constrained processing sequencing problem. With multiple objectives for the problem, random weights are generated to configure the fitness function. External elitist strategy is used and a K-means clustering algorithm is set to clip elite sets in order to keep the diversity of population. In this way, hybrid genetic algorithm is presented by designing crossover and mutation operation. The proposed algorithm can effectively solve the problem of multiple process optimization and decision problem with process constraints. Finally, a real case study is used to verify its effectiveness for solving flexible process planning issues.

Key words: process constraint, multi-objective optimization, genetic algorithm