基于集对分析与NSGA-Ⅱ的生产作业多目标优化

Production job multi-objective scheduling optimization based on set pair analysis and NSGA-Ⅱ

针对生产过程中生产作业的优化调度问题,以生产质量、效率和成本阈值为约束条件,基于集对分析建立了的生产质量—效率—成本控制的生产作业多目标优化模型;利用快速非支配排序遗传算法(NSGA-Ⅱ)求解优化模型,得到相对确定条件下质量—效率—成本控制的Pareto最优解集。决策者依据实际生产过程需要,为各项生产作业从Pareto最优解集中筛选最合理的调度方案。最后,通过算例仿真验证了结合集对分析与NSGA-Ⅱ的方法解决生产作业多目标优化问题的准确性、有效性和实用性。

To find the optimal scheduling for the production job in the production process, using the production quality, effi- ciency and cost threshold value as constraints, this paper built a multi-objective relative degree of nearness optimization model for quality-efficiency-cost control based on set pair analysis （SPA）. It solved the optimization model by using non-dominated sorting generic algorithm （NSGA-Ⅱ ） and acquired the Pareto-optimal solutions under the relative certainty conditions. Each job selected reasonable solution according to the optimal alternative. Finally, the experiment verifies the multi-objective opti- mization problems that illustrates the accuracy, effectiveness and applicability of the proposed method.