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针对柔性作业车间多目标调度问题,在考虑机器、操作人员等资源约束和交货日期不确定性的基础上,构建了以加工成本、客户满意度及生产总流程时间为目标函数的模糊调度数学模型。针对传统的加权系数方法不能很好地解决柔性作业车间调度多目标优化问题的缺点,提出改进的非支配排序遗传算法,采用改进的拥挤密度排序法改善同一非劣等级内个体的排序;提出自适应交叉和变异策略,克服了种群早熟化,改善了算法的收敛速度;采用改进精英策略保持种群多样性,改善了算法的搜索性能。将该算法应用于某机械公司的人机双资源多目标柔性车间模糊调度,仿真结果证明了该方法的有效性和可行性。
Aiming at the problem of multi-objective scheduling in flexible work shop, based on the resource constraint of machine and operator and the uncertainty of delivery date, a fuzzy scheduling math system is proposed, which takes processing cost, customer satisfaction and total production process time as objective functions model. The traditional weighted coefficient method can not solve the shortcomings of multi-objective optimization in flexible job shop scheduling. The improved non-dominated ranking genetic algorithm is proposed, and the improved ranking method is used to improve the ranking of individuals in the same non-inferior grade. Adapted to the crossover and mutation strategy, it overcomes the population prematureization and improves the convergence speed of the algorithm. The improved elite strategy is adopted to maintain the population diversity and improve the search performance of the algorithm. The algorithm is applied to the fuzzy scheduling of multi-objective flexible shop with dual human-machine resources in a mechanical company. Simulation results show the effectiveness and feasibility of the method.