基于混合果蝇优化算法的选址-库存联合优化策略研究
作者:
作者单位:

1.西安电子科技大学;2.中国人民解放军32272部队

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通讯作者:

中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Research on location-inventory Joint Optimization Strategy based on Hybrid Fruit Fly Optimization algorithm
Author:
Affiliation:

1.Xidian University;2.Unit 32272 of the Chinese People’s Liberation Army

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对装备维修保障仓库系统运营费用高、仓库点位布局不合理、备件库存结构不合理等问题,本文建立了以多品种联合补货问题为基础的装备维修备件仓库选址-库存控制决策联合优化模型,模型可用于求解仓库的开设位置、维修活动需求点的指派情况、仓库补货时间以及库存水平等。根据模型的结构特点,利用多种群协同进化的方法改进了传统果蝇优化算法的位置更新方式,设计了一种内外两层搜索策略的混合果蝇优化算法,外层搜索策略作为算法的主程序,用于搜索仓库选址决策变量,内层搜索策略采用改进的RAND算法,用于搜索库存控制决策变量。仿真结果表明混合果蝇优化算法具有良好的求解效率,确保库存系统在一定的服务水平的基础上,有效降低了库存运营总成本。

    Abstract:

    In view of the high operating cost of the equipment maintenance support warehouse system, unreasonable warehouse location layout, unreasonable spare parts inventory structure and so on, this paper establishes a joint optimization model of equipment maintenance spare parts warehouse location-inventory control decision-making based on multi-item joint replenishment problem. The model can be used to solve the warehouse location, demand point assignment, warehouse replenishment time and inventory level. According to the structural characteristics of the model, the traditional fruit fly optimization algorithm is improved by using the multi-swarm co-evolution feature, and a hybrid fruit fly optimization algorithm with internal and external search strategies is designed. The outer search strategy search for warehouse location decision variables, and the inner search strategy uses an improved RAND algorithm to search for inventory control decision variables. The simulation results show that the hybrid fruit fly optimization algorithm has good solution efficiency, which ensures that the inventory system can effectively reduce the total inventory operation cost on the basis of a certain service level.

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历史
  • 收稿日期:2021-02-01
  • 最后修改日期:2021-05-13
  • 录用日期:2021-06-03
  • 在线发布日期: 2021-07-01
  • 出版日期: