A stochastic theory of the firm
Mathematics of Operations Research
Computationally feasible bounds for partially observed Markov decision processes
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
Dynamic Programming and Optimal Control
Combined Pricing and Inventory Control Under Uncertainty
Operations Research
Adaptive Inventory Control for Nonstationary Demand and Partial Information
Management Science
Relationships Among Three Assumptions in Revenue Management
Operations Research
Revenue Management Through Dynamic Cross Selling in E-Commerce Retailing
Operations Research
Dynamic Pricing for Nonperishable Products with Demand Learning
Operations Research
Technical Note---Personalized Dynamic Pricing of Limited Inventories
Operations Research
Neural networks and Markov models for the iterated prisoner's dilemma
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Dynamic Pricing with a Prior on Market Response
Operations Research
Adaptive Strategies for Dynamic Pricing Agents
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A design model for knowledge-based pricing services in the retail industry
International Journal of Web Engineering and Technology
A design model for knowledge-based pricing services in the retail industry
International Journal of Web Engineering and Technology
Bayesian Dynamic Pricing in Queueing Systems with Unknown Delay Cost Characteristics
Manufacturing & Service Operations Management
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In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework to study a dynamic pricing problem faced by sellers of fashion-like goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer is to dynamically price the product in a way that maximizes expected revenues. Our model brings together various types of uncertainties about the demand, some of which are resolvable through sales observations. We develop a rigorous upper bound for the seller's optimal dynamic decision problem and use it to propose an active-learning heuristic pricing policy. We conduct a numerical study to test the performance of four different heuristic dynamic pricing policies in order to gain insight into several important managerial questions that arise in the context of revenue management.