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中国管理科学 ›› 2022, Vol. 30 ›› Issue (8): 143-154.doi: 10.16381/j.cnki.issn1003-207x.2020.0305

• 论文 • 上一篇    下一篇

随机需求下基于“自营+外包”模式的配送中心选址-配送问题研究

李珍萍, 仪明超   

  1. 北京物资学院信息学院,北京101149
  • 收稿日期:2020-03-01 修回日期:2020-07-14 出版日期:2022-08-18 发布日期:2022-08-18
  • 通讯作者: 李珍萍(1966-),女(汉族),山东平度人,北京物资学院信息学院,教授,博士,研究方向:物流系统优化,Email:lizhenping@bwu.edu.cn. E-mail:lizhenping@bwu.edu.cn
  • 基金资助:
    北京市自然科学基金资助项目(9212004,Z180005);国家自然科学基金资助项目(71771028);北京市属高校高水平创新团队支持计划项目(IDHT20180510)

Research on the Location-Distribution Problem of Distribution Centers Based on “Self-operating + Outsourcing” Mode Under Uncertain Demands

LI Zhen-ping, YI Ming-chao   

  1. School of Information, Beijing Wuzi University, Beijing 101149, China
  • Received:2020-03-01 Revised:2020-07-14 Online:2022-08-18 Published:2022-08-18
  • Contact: 李珍萍 E-mail:lizhenping@bwu.edu.cn

摘要: 针对顾客需求量不确定情况下末端配送中心选址及提前备货问题,提出了基于“自营+外包”配送模式的配送中心选址-配送问题。以自营配送中心的固定运行成本、提前备货成本和各种场景下的自营配送成本、外包配送成本以及缺货损失成本的期望值之和最小化为目标,建立了两阶段连续型随机规划模型。第一阶段确定自营配送中心的选址位置和各个配送中心的提前备货量;第二阶段确定各种场景下的自营配送货运量、外包配送货运量和客户点的缺货量等,使总成本期望值达到最小。基于Monte Carlo抽样理论设计了求解模型的样本均值近似方法;以及求解大规模问题L-shaped分解算法。通过模拟算例验证了两阶段随机规划模型的优越性和样本均值近似方法的有效性;并对自营配送中心固定运行成本、单位商品的自营配送成本和外包配送成本等进行灵敏度分析,得到了不同参数对应的最优配送策略,结果表明,正常情况下“自营+外包”配送模式是企业的最佳选择。本文同时将配送中心选址和提前备货量作为随机规划模型的第一阶段决策变量,可以帮助企业降低物流成本、提高顾客的满意度。

关键词: 随机需求;选址-配送;随机规划;样本均值近似;L-shaped算法

Abstract: In order to determine the locations of distribution centers and the quantity of stocks to be prepared ahead of time in each distribution center under uncertain demands of customers, the location-distribution problem with stochastic demand based on the “self-operating + outsourcing” distribution mode is proposed. A two-stage continuous stochastic programming model is established, the goal is to minimize the total costs including the fixed operating cost of distribution centers, the self-operating distribution cost from supply to the distribution centers, the inventory holding cost in the distribution centers, the expected self-operating distribution cost from distribution center to the customs, the outsourcing distribution cost and the shortage cost of customers. In the first stage, the locations of the self-operating distribution centers and the quantity of stocks to be prepared ahead of time in each distribution center are determined; In the second stage, the quantity of self-operating transportation and outsourcing transportation under the given scenario are respectively determined so as to minimize the sum of distribution cost and the shortage cost. Because it is difficult to solve the continuous stochastic programming model directly, the samples average approximation method based on Monte Carlo sampling simulation is proposed. The L-shaped algorithm for solving large-scale stochastic programming model is designed. The superiority of the two-stage stochastic programming model and the effectiveness of the samples average approximation algorithm are verified by simulation examples; Finally, the sensitivity analysis are done on the fixed operation cost of distribution center, the unit cost of self-operating distribution and the unit cost of outsourcing distribution. The optimal distribution strategies corresponding to various range of parameters value are obtained. The results show that, the “self-operating + outsourcing” distribution mode is the best strategy under most cases. The main contribution of this paper is that both the decision variables of the distribution centers location and the quantity of stock to be prepared ahead of time in each distribution center are included in the first stage of the stochastic programming model, which can help the enterprises to make more reasonable decisions, so as to reduce the total costs, shorten the delivery time and improve the customer’s satisfaction. d algorithm of this paper is efficifent for solving large-scale two-stage stochastic programming model, and can be extended to solving more complexity stochastic programming model. The model and algorithm of this paper can not only be used to solve the location-distribution problem of distribution center under the “self-operating + outsourcing” distribution mode, but also can help the E-commerce enterprises to detemine the quantity of goods to be transportated to the pre-warehouse near by the customers with uncertainty demands,so as to reduce response time for customers demand.

Key words: uncertain demand; location-distribution; stochastic programming; sample average approximation;L-shaped algorithm

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