An improved genetic algorithm based on multi-objective optimization is used to solve the flexible job-shop scheduling problem

This paper focuses on the multi-objective flexible job-shop scheduling problem. Because in the actual production process, the scheduling results from completion time, the machine load, cost control, resource consumption and other factors influence, so this paper puts forward a kind of improved genetic algorithm based on multi-objective optimization, to minimize the maximum completion time, minimize the machine load and minimize resource consumption three objective function optimization, combined with the improved Pareto multi-objective optimization method, the shortest processing time variation method and the neighborhood variation method, the optimization ability of the algorithm is improved. Finally, the experimental results show that the proposed algorithm is suitable for solving multi-objective flexible job-shop scheduling problem.