A Partially Observed Markov Decision Process for Dynamic Pricing

Yossi Aviv · Amit I. Pazgal

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Supply Chain and Inventory Management Consumer Market Behavior and Pricing Optimization and Search Problems

Abstract

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.

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References

1994Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons 1999Combined Pricing and Inventory Control Under Uncertainty 1997Periodic Pricing of Seasonal Products in Retailing 1993Inventory Control in a Fluctuating Demand Environment 1997Optimality of ( s , S ) Policies in Inventory Models with Markovian Demand 1999Stalking Information: Bayesian Inventory Management with Unobserved Lost Sales

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2012Pricing and Inventory Management 2007Relative Entropy, Exponential Utility, and Robust Dynamic Pricing 2008Retail Inventory Management When Records Are Inaccurate 2010Information Acquisition for Capacity Planning via Pricing and Advance Selling: When to Stop and Act?2016Chasing Demand: Learning and Earning in a Changing Environment 2019Responsive Pricing of Fashion Products: The Effects of Demand Learning and Strategic Consumer Behavior 2008Dynamic pricing and advertising for web content providers

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