An Effective Method to Solve Flexible Job-shop Scheduling Based on Cloud Model

In order to solve the problem of flexible job-shop scheduling, this paper proposed a novel quantum genetic algorithm based on cloud model. Firstly, a simulation model was established aiming at minimizing the completion time, the penalty and the total cost. Secondly, the method of double chains structure coding including machine allocation chain and process chain was proposed. The crossover operator and mutation operator were obtained by the cloud model X condition generator because of its randomness and stable tendency. The non dominated sorting strategy was introduced to obtain more optimal solution. Finally, the novel method was applied to the Kacem example and a mechanical mould scheduling, the simulation results demonstrated that the proposed method can reduce the precocious probability and obtain more non dominated solutions comparing with the existing algorithms.