关键词: 柔性车间调度, 多目标优化, 改进非支配排序遗传算法, 仿真

Abstract: To solve the multi-objective optimization problem in flexible Job Shop scheduling, a fuzzy scheduling mathematical model with objective functions of cost, total production cycle time and customer satisfaction was constructed by considering the resource constraints of machines and operators and the uncertainty of delivery date. Aiming at the problem that traditional weighted coefficient method could not solve the multi-objective scheduling optimization, an improved Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)was proposed. In this algorithm, an improved crowding density sorting method was used to ameliorate the individual sorting in same non-inferior grade; an adaptive crossover and mutation strategy was proposed to overcome the prematurity of population and to improve convergence speed. Improved elitism strategy was adopted to ensure the population diversity and improve the search performance. The algorithm was applied in a man-machine dual resource and multi-objective flexible workshop fuzzy scheduling in a machine company, and the feasibility and efficiency of algorithm were verified.

Key words: flexible workshop scheduling, multi-objective optimization, improved non-dominated sorting genetic algorithm, simulation