Scheduling Flexible Job Shop with Fuzzy Processing Time Using Co-evolutionary Genetic Algorithm

Fuzzy scheduling and flexible scheduling in job shop environment have been extensively considered, however, the problems with both flexible process plan and fuzzy processing conditions are seldom investigated for the high complexity. This paper addresses the scheduling problems of flexible job shop with fuzzy processing time. We developed an efficient co-evolutionary genetic algorithm (CGA) for the problems to minimize the fuzzy make span. CGA uses two-string representation with a real string and an integer string, a new decoding strategy and the co-evolutionary technique applied to chromosome. In each generation, both the evolution of only one string for some individuals and the evolution of two strings for other individuals occur. We conduct numerical experiments by using some instances to show the effectiveness of CGA. Computational results show that CGA performs better than the existing methods from literature.