Multi-objective flexible job shop scheduling method for machine tool component production line considering energy consumption and quality

A multi-objective flexible job shop scheduling model aiming at the completion time, idle time, processing quality and machine tool energy consumption is established according to the characteristics of machine tool components in production such as multi-varieties, small batch and large production energy consumption. And a genetic algorithm based on intuitionistic fuzzy set similarity (IFS_GA) is proposed to solve this scheduling model. The intuitionistic fuzzy set similarity value is used as the fitness value to lead the evolution of the algorithm. The crowd distance is used to trim the external files to improve the diversity of the population. In order to improve the quality of the initial population, a weight-based heuristic rule is proposed. A new chromosome cross method is presented to improve the searching ability of the algorithm. The leader is selected by the intuitionistic fuzzy set similarity value to guide the cross. In the feasible Pareto optimal solution, the solution with the highest similarity value of the intuitionistic fuzzy set is selected as the most satisfactory solution. The proposed algorithm is tested with the verification methods of example simulation, instance simulation and QUEST software. The results show that the IFS_GA is effective, and it is better than the NSGAII. © 2019, Editorial Office of Control and Decision. All right reserved.