东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (10): 1512-1516.DOI: 10.12068/j.issn.1005-3026.2016.10.030

• 管理科学 • 上一篇    下一篇

有限需求信息下基于最大熵原理的风险厌恶库存模型

邱若臻， 苑红涛， 冯俏   

  1. (东北大学 工商管理学院， 辽宁 沈阳110167)
  • 收稿日期:2015-06-29 修回日期:2015-06-29 出版日期:2016-10-15 发布日期:2016-10-14
  • 通讯作者: 邱若臻
  • 作者简介:邱若臻(1980-)，男，山东青岛人，东北大学副教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(71372186)； 教育部人文社会科学研究一般项目(11YJC630165)； 中央高校基本科研业务费专项资金资助项目(N150604005).

Risk Aversion Inventory Model Based on Maximum Entropy Approach Under Limited Demand Information

QIU Ruo-zhen， YUAN Hong-tao， FENG Qiao   

  1. School of Business Administration， Northeastern University， Shenyang 110167， China.
  • Received:2015-06-29 Revised:2015-06-29 Online:2016-10-15 Published:2016-10-14
  • Contact: QIU Ruo-zhen
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摘要： 针对风险厌恶的库存决策者，建立了基于条件风险值(CVaR)的单周期库存模型.在仅知需求区间、均值和方差信息情况下，采用最大熵原理估计了两种条件下的需求分布.结果显示，在仅知需求区间、均值和方差信息时，决策者应分别采用均匀和指数分布作为潜在的需求分布.在此基础上，进一步推导了基于CVaR的库存订货策略及其绩效情况.模拟结果表明，同真实需求分布下的最优情况相比，基于最大熵原理的库存策略虽然会导致绩效损失，但损失比例很小，表明基于最大熵原理的订货策略具有良好的鲁棒性.

关键词: 库存模型, 最大熵原理, 风险厌恶, 条件风险值, 鲁棒性

Abstract: A single period inventory model based on the conditional value-at-risk (CVaR) was developed for risk aversion decision-maker. Only considering demand interval， mean and variance information， the maximum entropy approach was used to estimate the demand distribution for both of the two demand uncertainties. The results showed that the decision-maker should adopt the uniform and exponential distribution as the potential demand distribution when only knowing the demand interval， and mean and variance information. On this basis， the CVaR-based inventory strategies and performances were deduced. The simulated results showed that the inventory strategy derived from the estimated distribution by maximum entropy will lead to a certain performance loss， however the loss ratio is very limited. It indicates that the ordering strategy based on the maximum entropy has good robustness.

Key words: inventory model, maximum entropy approach, risk aversion, conditional value-at-risk, robustness

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