Research on job shop scheduling based on ACM-GA algorithm

Abstract

An improved genetic algorithm with adaptive crossover and mutation probability (ACM-GA) was proposed for the job-shop scheduling problem with the optimization goal of minimizing the maximum completion time. The elite retention strategy is adopted to select individuals. The improved preemptive crossover operator PPX and the crossover probability with adaptive ability are referenced to carry out crossover operation. The mutation operator based on neighborhood search and the mutation probability with adaptive ability are used to carry out the mutation operation. Finally, using the same examples in the literature, the results of this paper are compared with those in the literature, and the feasibility and effectiveness of the proposed algorithm are verified.