A Maximum Entropy Joint Demand Estimation and Capacity Control Policy

25 Pages Posted: 26 Apr 2014

See all articles by Costis Maglaras

Costis Maglaras

Columbia University - Columbia Business School, Decision Risk and Operations

Serkan Eren

Independent

Multiple version iconThere are 2 versions of this paper

Date Written: April 11, 2014

Abstract

We propose a tractable, data-driven demand estimation procedure based on the use maximum entropy (ME) distributions, and apply it to a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare-class "Littlewood" problem in a setting where the firm has access to only potentially censored sales observations; this is also known as the repeated newsvendor problem. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution. When the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the optimal protection level. That is, the proposed heuristic avoids the "spiral down" effect, making it attractive for problems of joint forecasting and revenue optimization problems in the presence of censored observations.

Keywords: Revenue management, censored demand, uncensoring, maximum entropy distributions

JEL Classification: C44

Suggested Citation

Maglaras, Costis and Eren, Serkan, A Maximum Entropy Joint Demand Estimation and Capacity Control Policy (April 11, 2014). Available at SSRN: https://ssrn.com/abstract=2428927 or http://dx.doi.org/10.2139/ssrn.2428927

Costis Maglaras (Contact Author)

Columbia University - Columbia Business School, Decision Risk and Operations ( email )

New York, NY
United States

Serkan Eren

Independent ( email )

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