现代制造工程 ›› 2017, Vol. 442 ›› Issue (7): 114-120.doi: 10.16731/j.cnki.1671-3133.2017.07.023

• 制造技术/工艺装备 • 上一篇    下一篇

基于改进遗传算法的物料配送多目标优化模型研究浙江省自然科学基金项目(LY12G02015)

陈广胜, 董宝力   

  1. 浙江理工大学机械与自动控制学院,杭州 310018
  • 收稿日期:2016-05-13 出版日期:2017-07-18 发布日期:2017-09-29
  • 作者简介:陈广胜,硕士研究生,主要研究方向为精益生产。董宝力,副教授,博士,已发表论文十余篇,主要研究方向为精益生产及管理信息系统。E-mail:sdkj_cgsheng@163.com

Research on material distribution multi-objective optimization model based on improved genetic algorithm

Chen Guangsheng, Dong Baoli   

  1. Faculty of Mechanical Engineering & Automation,Zhejiang Sci-Tech University,Hangzhou 310018,China
  • Received:2016-05-13 Online:2017-07-18 Published:2017-09-29

摘要: 为解决装配车间物料配送过程中路径以及作业者数量不易确定的问题,以配送总时间和作业者数量为目标,同时考虑作业者的作业效率以及物料车的装载率,提出一种装配车间物料配送多目标优化模型,并采用改进遗传算法对模型进行求解。为解决多目标优化遗传算法求解过程中收敛速度慢且存在大量非可行解的问题,在算法设计过程中,将作业者配送路径和任务分配顺序进行实值分段编码,并在算法的交叉和变异中增加非可行解检验过程。以叉车装配为例,验证了物料配送优化模型的有效性。

关键词: 装配车间, 物料配送, 作业者数量, 改进遗传算法

Abstract: To solve the problem of various material distribution routes and uncertain operators in assembly shop,considering the utilization of operators and the loading rate of material vehicle,an optimization model of lean material distribution was proposed with the target of minimizing total distribution time and the number of operators.The improved genetic algorithm was used to solve this model.In order to solve the problem that the multi-objective optimization genetic algorithm difficult to converge in practice and have a lot of illegal solution,in the design process of algorithm,it uses integer coding to reflect directly operators distribution routing and allocation of tasks,and add illegal solutions for inspection in crossover and mutation.The example verifies feasibility and effectiveness of optimization model.

Key words: assembly shop, material distribution, the number of operators, improved genetic algorithm

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