引用本文:李益兵,宋东林,王磊.基于混沌遗传算法的集团分布式制造工序资源配置[J].控制与决策,2019,34(6):1178-1186
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基于混沌遗传算法的集团分布式制造工序资源配置
李益兵,宋东林, 王磊
(1. 武汉理工大学机电工程学院,武汉430070)
摘要:
集团分布式制造企业往往存在着地理位置不集中、制造资源和制造能力不均衡、资源闲置与资源短缺并存等问题,针对集团制造企业在制造资源配置过程中多主体、多任务、多资源、多工序以及协同性的特点,从集团公司总体利益及下属企业个体利益多角度出发,综合考虑生产成本、加工资源、加工效率等多个因素,建立集团分布式制造资源配置优化模型,并采用基于Logistic混沌改进的遗传算法求解该模型的Pareto最优解.最后对国内某建材装备集团的制造资源配置过程进行算例分析,以验证模型和算法的有效性.
关键词:  集团分布式制造  工序级资源配置  混沌改进  遗传算法  多目标优化  Pareto最优
DOI:10.13195/j.kzyjc.2017.1526
分类号:TP18
基金项目:国家自然科学基金项目(71171154);中央高校基本科研业务费专项资金项目(2017II27GX).
Group distributed manufacturing process resource allocation based on chaos genetic algorithm
LI Yi-bing,SONG Dong-lin,WANG Lei
(1.School of Mechanical and Electronic Engineering,Wuhan University of Technology,Wuhan430070,China)
Abstract:
There are some problems in group distributed manufacturing enterprises, such as the scatter of geographically location, the mismatching of manufacturing resources and abilities, the coexistence of resources idle and shortage etc. Based on the characteristics of multi-agent, multi-task, multi-resource, multi-process and co-ordination in the process of manufacturing resource allocation, the optimal model of distributed manufacturing resource allocation is proposed to balance the overall interests of the group and the individual interests of the sub-ordinate enterprises. An improved genetic algorithm based on Logistic chaos is designed to obtain the Pareto optimal solution of the model. Finally, an example with its analysis is given to demonstrate the effectiveness of the proposed model and algorithm.
Key words:  group distributed manufacturing  process-level resources allocation  chaos-improved algorithm  genetic algorithm  multi-objective optimization  Pareto optimal solution

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