Research on Multi-objective Fuzzy Flexible Job-Shop Scheduling Based on Cloud Computinger

Aiming at the flexible job-shop scheduling problem in fuzzy environment, targeting at minimizing the average completion time and maximizing the customer satisfaction, mathematical model of flexible job-shop scheduling problem is established, and cloud adaptive genetic algorithm is proposed. Aiming at the characteristics of flexible operation, double-chain quantum coding method for machine distribution chain and workpiece process chain is proposed; Aiming at the problems that the crossover and mutation operation of genetic algorithm may lead to premature convergence and late diversity loss, cloud computing method is used to design cloud crossover operator and cloud mutation operator for operation, and improved cloud adaptive genetic algorithm is proposed. Through the example of classical job-shop scheduling, it is verified that the proposed algorithm can reduce the precocious probability and improve the iterative search efficiency, and more non-dominated solution can be obtained compared with other algorithms.