GA–based integrated scheduling with a new crossover

Abstract: Traditional job shop scheduling problems generally supposes that there is a single feasible process plan with which a part can be processed in a shop. The assumption is hardly true in today’s flexible manufacturing environment. It has great significance to solve process planning and scheduling simultaneously. A mathematical model was formed but traditional solving methods were inefficient. We describe a modified Genetic Algorithm (GA) to generate scheduling scheme efficiently. A new crossover (VRX) was designed specially for the problem of alternative route scheduling to ensure the legality of the process plan. Also, knowledge-based methods and adaptive strategies were used to enhance the searching performance. At last, two examples were given to test the presented algorithm and the results appear to much better by contrast with the results of simple heuristics and methods without using knowledge