The improved simulated annealing genetic algorithm for flexible job-shop scheduling problem

Abstract

An improved simulated annealing genetic algorithm (ISAGA) was proposed to solve the complex flexible job-shop scheduling problem (FJSP). In ISAGA, the coding method was based on the combination of working procedure coding and machine allocation coding. In the process of crossover, the improved multi-parent process crossover (IMPC) was proposed. The cloud model theory and the simulated annealing algorithm were introduced in the process of mutation. The X conditional cloud generator in cloud model theory was used to generate the mutation probability in genetic operation. The simulated annealing operation was carried out on the variability of results. In order to avoid the loss of the optimal solution, the optimal individual repository (OIR) was used to store the optimal solution in the process of crossover and mutation. Overcoming the shortcomings of genetic algorithm premature convergence and slow convergence, the experimental results indicated that the proposed algorithm could solve the FJSP effectively and efficiently.