• CN:11-2187/TH
  • ISSN:0577-6686

›› 2010, Vol. 46 ›› Issue (17): 114-122.

• 论文 • 上一篇    下一篇

混流加工/装配系统集成优化研究

王炳刚   

  1. 河南城建学院工商管理系
  • 发布日期:2010-09-05

Research on Integrated Optimization for Mixed-model Fabrication/assembly Systems

WANG Binggang   

  1. Department of Industry and Business Administration, Henan University of Urban Construction
  • Published:2010-09-05

摘要: 为解决由一条混流装配线和一条带相同并行机和有限中间缓冲区的部件加工线组成的拉式生产系统的集成优化问题,以平顺化混流装配线的部件消耗以及最小化加工线最大完工时间为优化目标,建立加工/装配系统集成优化框架和装配线优化数学模型,提出加工线调度方案的构造方法,设计一种多目标遗传算法用于求解该问题,在此算法中,提出一种三阶段的实数编码方法,同时将帕累托分级和共享函数的方法用于可行解适应度值的评价,并对选择、交叉、变异算子以及精英解保留策略进行设计,保证了非支配解集中个体分布性和均匀性。通过与多目标模拟退火算法的结果进行比较,证明了该多目标遗传算法的可行性和有效性,应用该算法可以获得满意的非支配解集。

关键词: 多目标模拟退火算法, 多目标遗传算法, 混流加工/装配系统, 集成优化

Abstract: The integrated optimization problems for pull production systems composed of one mixed-model assembly line and one part fabrication line with identical parallel machines and limited intermediate buffers are investigated. Two objectives are considered simultaneously:Minimizing the total variation in parts consumption in the assembly line and minimizing the makespan in the fabrication line. The integrated optimization framework, the mathematical model for the assembly line and a procedure to construct a complete schedule for the fabrication line are presented. A multi-objective genetic algorithm (MOGA) is proposed for solving the problem, in which a three-stage real number encoding method is put forward, the Pareto ranking method and the sharing function method are employed to evaluate the individuals’ fitness, the selection, crossover, mutation operators and the elitist strategy are designed, which guarantees the dispersity and uniformity of the solutions. The feasibility and efficiency of the MOGA is shown by comparison with a multi-objective simulated annealing algorithm (MOSA). The computational results show that satisfactory non-dominated solution set can be obtained by the MOGA.

Key words: Integrated optimization, Mixed-model fabrication/assembly system, Multi-objective genetic algorithm, Multi-objective simulated annealing algorithm

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