Some monotonicity results for partially observed Markov decision processes
Operations Research
A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Inventory control in a fluctuating demand environment
Operations Research
Sunoptimal policies, with bounds, for parameter adaptive decision processes
Operations Research
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
A Partially Observed Markov Decision Process for Dynamic Pricing
Management Science
Real-time supply chain control via multi-agent adjustable autonomy
Computers and Operations Research
A Cultural Algorithm for POMDPs from Stochastic Inventory Control
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Information Relaxations and Duality in Stochastic Dynamic Programs
Operations Research
Manufacturing & Service Operations Management
A belief-rule-based inventory control method under nonstationary and uncertain demand
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
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.