Adaptive Inventory Control for Nonstationary Demand and Partial Information
研究需求过程非平稳且部分可观测的库存控制问题，比较多种控制策略，发现某些实用策略几乎总是优于常用的确定性等价控制策略。
This paper examines several different policies for an inventory control problem in which the demand process is nonstationary and partially observed. The probability distribution for the demand in each period is determined by the state of a Markov chain, the core process. However, the state of this core process is not directly observed, only the actual demand is observed by the decision maker. Given this demand process, the inventory control problem is a composite-state, partially observed Markov decision process (POMDP), which is an appropriate model for a number of dynamic demand problems. In practice, managers often use certainty equivalent control (CEC) policies to solve such a problem. However, this paper presents results that demonstrate that there are other practical control policies that almost always provide much better solutions for this problem than the CEC policies commonly used in practice. The computational results also indicate how specific problem characteristics influence the performance of each of the alternative policies.