A novel hybrid method for solving flexible job-shop scheduling problem

Abstract

For the purpose of solving the flexible job-shop scheduling problem (FJSP), an improved quantum genetic algorithm based on earliness/tardiness penalty coefficient is proposed in this paper. For minimizing the completion time and the job-shop cost, a simulation model was established firstly. Next, according to the characteristics of the due in production, a double penalty coefficient was designed and a double chains coding method was proposed. At last, the effectiveness of the proposed method is verified through being applied to the Kacem example and compared with some existing algorithms.