Optimization of Fuzzy Job-shop Scheduling with Multi-process Routes and its Co-evolutionary Algorithm

This paper studies the job-shop scheduling problems of multi-process routes with fuzzy processing time and fuzzy due date, and establishes a fuzzy scheduling model to reach the optimization goals of maximum average satisfaction index. Then a co-evolutionary algorithm combined with genetic algorithm and discrete particle swarm optimization algorithm is presented, which improves the chromosome encoding scheme based on working procedure, with the idea of collaboration and the mechanism of feedback, this algorithm guides the evolutionary process of two populations which respectively use genetic algorithm and discrete particle swarm optimization algorithm in an effective and complementary way. The simulation results show that the co-evolutionary algorithm is a feasible method.