基于改进NSGA-Ⅱ算法的柔性车间调度问题研究  

Research on Flexible Workshop Scheduling Problem Based on Improved NSGA-Ⅱ Algorithm

作　　者：李政 于正林[1] 邵长顺 LI Zheng;YU Zhenglin;SHAO Changshun(School of Mechanical and Electrical Engineering,Changchun University of Science and Technology,Changchun 130022)

机构地区：[1]长春理工大学机电工程学院,长春130022

出　　处：《长春理工大学学报（自然科学版）》2024年第2期44-52,共9页Journal of Changchun University of Science and Technology(Natural Science Edition)

基　　金：科技部2020重点专项(2020YFB1712202)。

摘　　要：研究了柔性车间调度中双目标调度优化问题,以最小化最大完工时间和最小化机器空载率为优化目标,基于生产机加车间产线建立数学模型。选取NSGA-Ⅱ(Non-dominated Sorting Genetic AlgorithmsⅡ,NSGA-Ⅱ)算法作为基础算法,在此基础上提出基于反向学习的NSGA-Ⅱ算法(简称OBL-NSGA-Ⅱ),通过引入反向种群,增加种群的多样性,保证了解的质量,能够有效避免算法迭代过程中由于种群多样性降低导致算法陷入局部最优的问题。最后通过Matlab仿真软件进行了对比实验,验证了所提算法的有效性。This paper mainly studies the optimization problem of dual-objective scheduling in flexible workshop scheduling,and establishes a mathematical model based on the production line of production machine processing workshop with the optimization goal of minimizing the maximum completion time and minimizing the machine no-load rate.NSGA-Ⅱ(Non-dominated Sorting Genetic AlgorithmsⅡ.,NSGA-Ⅱ.)algorithm is selected as the basic algorithm,and on this basis,the NSGA-Ⅱ.algorithm based on reverse learning(abbreviated as OBL-NSGA-Ⅱ.)is proposed.By introducing the reverse population,i ncreasing the diversity of the population and ensuring the quality of answer,the algorithm can effectively avoid falling into the local optimal problem due to the decrease of population diversity in the algorithm iteration process.Finally,the effectiveness of the proposed algorithm is verified by Matlab simulation software.

关 键 词：NSGA-Ⅱ算法 反向学习 双目标调度优化 种群多样性 

分 类 号：TH165[机械工程—机械制造及自动化]