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中国管理科学

中国管理科学 ›› 2011, Vol. 19 ›› Issue (1): 77-83.

• 论文 • 上一篇    下一篇

基于遗传算法的虚拟企业协同资源优化问题研究

齐二石, 李辉, 刘亮   

  1. 天津大学管理学院 天津 300072
  • 收稿日期:2010-05-17 修回日期:2011-01-06 出版日期:2011-02-28 发布日期:2011-02-28
  • 作者简介:齐二石(1953- ),男(汉族),吉林长春人,天津大学管理学院院长,教授,博士,博士生导师,研究方向:现代工业工程理论与应用、物流工程与供应链管理、制造企业信息化工程.
  • 基金资助:

    国家自然科学基金面上项目(70671072)

Research on Collaborative Resource Optimization of Virtual Enterprises Based on Genetic Algorithm

QI Er-shi, LI Hui, LIU liang   

  1. School of Management, Tianjin University, Tianjin 300072, China
  • Received:2010-05-17 Revised:2011-01-06 Online:2011-02-28 Published:2011-02-28

PDF (PC)

1467

被引次数

30

摘要： 面向复杂零件的协同制造,以工艺流程为核心将协同制造任务进行分解,并有效利用"逻辑制造单元"和"逻辑加工路线"等概念描述复杂零件的协同制造任务,最终目标是形成基于复杂零件工艺流程的、可支撑异地协同生产的加工路线。对复杂零件协同制造的制造资源优化配置问题进行了数学分析和描述,建立了问题的目标函数与约束条件。本文以加工时间、运输费用和加工质量作为目标,约束条件包括顺序约束、释放期约束、时间约束、交货期约束、成本约束和质量约束,最终将资源优化配置问题归结为多目标优化问题,并利用遗传算法进行求解,得到了较为满意的结果。通过实例分析,将模型应用于某型号发动机叶片协同制造,说明采用本论文的模型可以有效解决复杂零件协同制造的资源优化配置问题。

关键词: 协同制造, 资源优化配置, 多目标优化, 遗传算法

Abstract: In order to solve resource optimizing deployment problem for cooperative manufacturing of complex parts, the cooperative manufacturing task is decomposed by taking process flow as core.The concepts such as LMU (logical manufacturing unit) and LMP (logical manufacturing process), etc.are used to describe cooperative manufact uring task of complex parts.The ultimate goal is to establish a process routing that can support cooperative manufacturing based on process flows of complicated parts.The objective function and constraints of the problem are established.The problem of resource optimizing deployment is converted to multi-objective optimization model, with processing time, delivery expense and processing quality as objectives, and sequence, release time, production time, time of delivery, cost and quality as constraints, and is solved by genetic algorithm.At last, the model is used for the coordination manufacturing of an generator engine.This example proves the feasibility and validity of the method.

Key words: collaborative manufacturing, resource optimizing deployment, multi-objective optimization, genetic algorithm (GA)

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