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中国管理科学 ›› 2021, Vol. 29 ›› Issue (2): 108-116.doi: 10.16381/j.cnki.issn1003-207x.2021.02.012

• 论文 • 上一篇    下一篇

延迟支付条件下易逝品的最优订货决策研究

徐贤浩1, 王倩1, 曾款2, 彭红霞3   

  1. 1. 华中科技大学管理学院, 湖北 武汉 430074;
    2. 武汉纺织大学管理学院, 湖北 武汉 430200;
    3. 湖北大学商学院, 湖北 武汉 430063
  • 收稿日期:2018-04-27 修回日期:2018-09-07 发布日期:2021-03-04
  • 通讯作者: 曾款(1988-),男(汉族),江西萍乡人,武汉纺织大学管理学院,讲师,博士,研究方向:供应链金融、互联网金融等,E-mail:kzeng@wtu.edu.cn. E-mail:kzeng@wtu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(71471071,71620107002)

Study on the Optimal Ordering Policy of Perishable Products with Delayed Payment

XU Xian-hao1, WANG Qian1, ZENG Kuan2, PENG Hong-xia3   

  1. 1. Management School, Huazhong University of Science & Technology, Wuhan 430074, China;
    2. Management School, Wuhan Textile University, Wuhan 430200, China;
    3. Business School, Hubei University, Wuhan 430063, China
  • Received:2018-04-27 Revised:2018-09-07 Published:2021-03-04

摘要: 本文在考虑延期支付的情况下构建了二级供应链关于易逝品的最优订货决策模型,其中供应链由单个供应商和多个面临资金约束的零售商组成。在不允许缺货的情况下,供应商为零售商提供延迟支付来缓解零售商的资金压力,同时零售商对产品的定价将随易逝品的价值变质而变化。本文利用遗传算法对模型进行了算例分析,同时对模型中的相关参数进行了敏感性分析。结果表明,延迟支付能够提高供应链总利润并降低风险,从而有利于供应链整体效益的提高。在延期支付条件下,供应链各成员获得的收益增量存在较大差距,其中供应商能够获得更多收益。最后,通过敏感性分析,我们还发现产量及易逝品保质期的变动会对供应链整体利润产生不同程度的影响。

关键词: 延迟支付, 易逝品, 订货决策, 遗传算法

Abstract: The optimal ordering policy of perishable products in a two-level supply chain with a single-supplier and capital-constrained multi-retailers is investigated, in which the supplier allows the delayed payment. It is considered there is no shortage allowed, and the price changes with the customer's willingness to buy the perishable product. Through the numerical test using genetic algorithm (GA), the optimal ordering policy is found out. The results show that the delayed payment helps to improve the whole supply chain efficiency, which would increase the profits for each supply chain member while lowering the risk, and the supplier benefits more from the delayed payment. Finally, the sensitivity analysis is conducted to discuss the effect of different parameters on the total profits, which provides management insight forordering decisions.

Key words: delay in payment, perishable products, ordering policy, Genetic Algorithm

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