顾客需求量不确定时生产商的在线数量折扣策略

运筹与管理 ›› 2011, Vol. 20 ›› Issue (2): 170-175.

顾客需求量不确定时生产商的在线数量折扣策略

张文明^1,2, 徐寅峰^1,2, 苏兵³

1.西安交通大学管理学院,陕西西安 710049;
2.西安交通大学机械制造系统工程国家重点实验室,陕西西安 710049;
3.西安工业大学经济管理学院,陕西西安 710032

收稿日期:2009-11-11 出版日期:2011-04-25
作者简介:张文明,男,山东潍坊人,博士研究生,研究方向:供应链协调与金融投资策略。
基金资助:
国家自然科学基金项目(70525004,60736027)

Online Quantity Discount Strategy for Producers with Uncertain Demand of Customers

ZHANG Wen-ming^1,2, XU Yin-feng^1,2, SU Bing³

1. School of Management, Xi’an Jiaotong University Xi’an 710049, China;
2. The State Key Lab for Manufacturing Systems Engineering, Xi’an 710049, China;
3. School of Economics and Management, Xi’an Technological University, Xi’an 710032, China

Received:2009-11-11 Online:2011-04-25

摘要/Abstract

摘要： 针对顾客需求量不确定时,生产商为节省库存成本而采用数量折扣策略鼓励零售商提高单次订货量的决策问题,从在线问题与竞争分析的角度出发,设计了平衡策略,并证明了该策略是最优在线数量折扣策略。然后给出了算例,算例表明了无论顾客的需求量如何变化,平衡策略确定出的数量折扣,总能使生产商的成本接近于顾客需求量确定时的最小成本;同时与传统的数量折扣模型进行了对比,说明了在传统的数量折扣模型中做出的决策,往往不是我们所提出的新的评价准则(竞争比)下的最优策略。

关键词: 供应链, 数量折扣, 在线策略, 竞争比, 需求量不确定

Abstract: The problem for the producer is how to design the discount strategy to inspire the retailer to increase his single orders when customers’ demand is uncertain. From the online point of view, Balance-Strategy is proposed in this paper. The strategy not guarantees only the acceptance of the discount for the retailer, but also the cost of the producer as small as possible. Examples are also presented in this paper which show that no matter how the demand changes using Balance-Strategy can guarantee the cost always close to the minimum cost which knows the demand beforehand. Moreover, we compare our model with the traditional ones by examples and show that the decisions made by the traditional models may not be the optimal ones from the competitive view.

Key words: supply chain, quantity discount, online strategy, competitive ratio, uncertain demand

中图分类号:

F406. 2

张文明, 徐寅峰, 苏兵. 顾客需求量不确定时生产商的在线数量折扣策略[J]. 运筹与管理, 2011, 20(2): 170-175.

ZHANG Wen-ming, XU Yin-feng, SU Bing. Online Quantity Discount Strategy for Producers with Uncertain Demand of Customers[J]. Operations Research and Management Science, 2011, 20(2): 170-175.

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