中文摘要：

采用多目标进化算法研究柔性作业车间调度问题,目标是最小化最大完工时间、机器总负荷和最大机器负荷3个性能指标.针对NSGA-Ⅱ识别非支配个体较慢和个体比较次数较多的不足,设计一种基于预排序的快速非支配排序算法,快速识别非支配个体并淘汰被支配个体,提高非支配解集的构造效率;结合柔性作业车间调度问题的特点和进化算法的性能,引入云模型进化策略,提出一种基于非支配排序的云模型进化多目标柔性作业车间调度算法.运用云模型揭示模糊性和随机性的优良特性维护进化种群,提高非支配解分布的广度和均匀度.利用多指标加权灰靶决策模型选择最满意调度方案.使用基准实例进行测试并比较测试结果,验证了算法的可行性和有效性;利用提出算法确定了生产实际的最满意调度方案.

英文摘要：

A multi-objective evolutionary algorithm is applied to research the flexible job-shop scheduling problem,which sets maximum makespan,the total workload of machines and maximum workload of machine as optimization goals.Aiming at improving performance of NSGA-Ⅱ at distinguishing and comparing non-dominated individuals,a modified non-dominated sorting algorithm is designed so that it can distinguish non-dominated individuals rapidly,eliminate dominated individuals and enhance the conformation efficiency of non-dominated sets.Combining the characteristics of flexible job-shop scheduling problem and properties of evolutionary algorithm,and introducing evolutionary strategy based on cloud model,a multi-objective flexible job-shop scheduling algorithm based on improved non-dominated sorting is proposed.Applying the excellent characteristics of cloud model in both fuzziness and randomness to maintain evolutionary populations and ameliorate the distribution breadth and uniformity of non-dominated solution.Making use of multi-attribute decision model based on weighted grey target to select the most satisfied schedule.Using the proposed algorithm to test the benchmark problems and analyze the results,and testified its feasibility and effectiveness.The proposed algorithm is applied to a real production case study,and the most satisfied schedule is generated.