中国机械工程

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基于粒子群算法的多目标可重构设施布局方法

丁祥海;姚文鹏   

  1. 杭州电子科技大学工业工程与管理研究所,杭州,310018
  • 出版日期:2017-04-10 发布日期:2017-04-07
  • 基金资助:
    国家社会科学基金资助项目(15BGL100);
    NSFC-浙江两化融合联合基金资助项目(U1509220);
    浙江省自然科学基金资助项目(LY13G010007);
    浙江省人文社科基地资助重点项目(ZD05-2016ZB);
    浙江省哲学社会科学重点研究基地浙江省信息化与经济社会发展研究中心资助项目(15XXHJD11)

Multi-objective Reconfigurable Facility Layout Method Based on Particle Swarm Optimization

DING Xianghai;YAO Wenpeng   

  1. Institute of Industrial Engineering and Management,Hangzhou Dianzi University, Hangzhou,310018
  • Online:2017-04-10 Published:2017-04-07

摘要: 提出了一种多目标可重构设施布局方法。该方法引入了空间填充曲线来表征设施位置,可以实现任意两个设施之间的互换;考虑了柔性面积需求和设施形状约束系数等因素,保证布局方案的可行性;建立了以成本(物料运输成本和设施重构成本)和在制品库存为目标的多目标可重构设施布局模型;设计了该模型的改进粒子群算法,该算法在全局极值和个体极值的选取、Pareto解集的更新策略方面相对于标准的粒子群算法有改进。最后用算例说明了该方法的有效性。

关键词: 多目标可重构设施布局, 改进粒子群算法, 设施形状约束系数, Pareto解集

Abstract: A method of multi-objective reconfigurable facilities layout was presented. The space filling curve was used to describe the facilities locations and it was possible to exchange between any two facilities. The flexibility area requirements of facility and the facility's shape constraint coefficient were used to keep the layout solution's feasibility. A multi-objective reconfigurable facilities layout model with costs (material transportation costs and facility reconstruction costs) and works in processes (WIP) as targets was established. An improved particle swarm optimization algorithm was designed for this model. Compared with standard particle swarm optimization the algorithm was improved in the selection of global extremum and individual extremum, and the updating strategy of Pareto solution set. Finally, an example was given to illustrate the effectiveness of this method.

Key words: multi-objective reconfigurable facility layout, improved particle swarm optimization algorithm, facility shape constraint coefficient, Pareto set

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