Abstract: Flexible job shop scheduling problems have the characteristics of diversified solution sets and complex solution spaces. Traditional multi-objective optimization algorithms may fall into local optimality and lose the diversity of solutions when solving those problems. In the case of establishing a flexible job shop scheduling model with the maximum completion time, maximum energy consumption and total machine load as the optimization goals, an improved non-dominated sorting genetic algorithm (INSGA-II) was proposed to solve this problem. Firstly, the INSGA-II algorithm  combines random initialization and heuristic initialization methods to improve population diversity. Then it adopts a targeted crossover and mutation strategies for the process part and the machine part to improve the algorithm’s global searching capabilities. Finally,  adaptive crossover and mutation operators are designed to take into account the global convergence and local optimization capabilities of the algorithm. The experimental results on the mk01～mk07 standard data set show that the INSGA-II algorithm has better algorithm convergence and solution set diversity.

Key words: flexible job shop scheduling, multi-objective optimization, non-dominated sorting genetic algorithm