Flexible Job Shop Scheduling of Machining Based on Multi- objective Differential Evolution Algorithm

Flexible job shop scheduling is difficult, time-consuming and cost-effective due to its variety and number of pieces. In this paper, a multi-objective flexible job shop scheduling model for machining was established with the optimization objectives of maximum completion time, energy consumption and tool loss, considering rework, preparation time of sequence and batch scheduling constraints. A multi-objective flexible job shop scheduling model considering energy consumption and tool loss was proposed, and a machine selection strategy was proposed to improve the differential evolution algorithm. The improved differential evolution algorithm was applied to machining flexible job shop scheduling. Compared with the traditional differential evolution algorithm, the improved differential evolution algorithm has the advantages of faster convergence speed and better robustness, and the optimized machine load is more balanced, which can effectively solve the multi-objective machining flexible job shop scheduling problem, and provide a good guidance for multi variety and multi piece scheduling tasks plan.