计算机工程与应用 ›› 2017, Vol. 53 ›› Issue (17): 241-248.DOI: 10.3778/j.issn.1002-8331.1603-0334

• 工程与应用 • 上一篇    下一篇

基于模糊决策与进化算法的生产设备布局优化

高贵兵1,岳文辉2,张道兵1   

  1. 1.湖南科技大学 机电工程学院,湖南 湘潭 411201
    2.湖南科技大学 机械设备维护湖南省重点实验室,湖南 湘潭 411201
  • 出版日期:2017-09-01 发布日期:2017-09-12

Optimizing production equipment layout based on fuzzy decision and evolutionary algorithm

GAO Guibing1, YUE Wenhui2, ZHANG Daobing1   

  1. 1.College of Mechanical and Electrical Engineering, Hunan University of Science & Technology, Xiangtan, Hunan 411201, China
    2.Hunan Provincial Key Laboratory of Mechanical Equipment Maintenance, Hunan University of Science & Technology, Xiangtan, Hunan 411201, China
  • Online:2017-09-01 Published:2017-09-12

摘要: 为解决复杂情况下制造系统的生产设备布局优化问题,提出了一种将模糊决策与进化算法相结合的设备布局优化方法。进一步完善了优化模型,优化目标包括总成本最小、设备相邻要求最大化和面积利用率最大化等优化目标;其中总成本最小目标考虑了物料搬运成本,设备重置导致的设备拆装、移动成本,生产停工造成的产能损失成本。该方法考虑了用户对于成本、利用率及相邻性要求等存在的满意度、优先度等模糊情况,基于模糊决策理论,对多目标优化模型进行了模糊化处理,设计了模糊适应度函数,用以根据用户的优先关系评价pareto解集。基于求解模型的特点,对多目标进化算法的染色体编码方式与交叉、变异等遗传操作方式进行改进,以提高求解该模型的实用性与效率。最后以实际案例的优化结果证明了该方法的有效性。

关键词: 模糊决策, 进化算法, 布局优化, 多目标优化

Abstract: An improved optimization method is proposed to solve the optimization problem of production equipment layout of manufacturing system, which the variety of complex situation is considered, and both the fuzzy decision and evolutionary algorithm are combined.Firstly, the optimization model is improved, that the total cost is minimized, while the requirements of adjacent equipment and the space utilization are maximized; the material handling cost, the resetting cost, the loss of production costs are considered in the total cost objective.Secondly, The blur of satisfaction, priority which existed in the targets, such as cost, efficiency and neighboring requirements, are considered in this method; and based on the fuzzy decision theory, the model is fuzzy processing; the fuzzy fitness function is designed to evaluate the Pareto solution set.Thirdly, the operation of coding chromosome, crossover and mutation in the multi-objective evolutionary algorithm are improved to solve the multi-objective optimization model.Finally the optimization results of an actual case demonstrate the validity of the model and algorithm.

Key words: fuzzy decision-making, evolutionary algorithms, facility layout optimization, multi-objective optimization