JSM 2004 - Toronto

Abstract #301809

This is the preliminary program for the 2004 Joint Statistical Meetings in Toronto, Canada. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2004); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

Back to main JSM 2004 Program page
Activity Number: 308
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #301809
Title: Revenue Management with Correlated Demand Forecasting and Multistage Stochastic Programming
Author(s): Catalina Stefanescu *+ and Kristin Fridgeirsdottir and Victor de Miguel and Stefanos Zenios
Companies: London Business School and London Business School and London Business School and Stanford University
Address: Regent's Park , London , International, NW1 4SA, United Kingdom
Keywords: revenue management ; forecasting ; EM algorithm ; multistage stochastic programming
Abstract:

A popular approach to airline revenue management is to assign a bid price to each resource in the network. Bid prices are estimates of the expected future revenues, generated in a two-step process. First, a demand forecasting model is estimated from historical demand data. Second, this model is used to generate a scenario tree of future travel demand, and the expected future revenue is maximized with an optimization algorithm. This process is repeated daily to compute bid prices and make booking decisions. We propose a revenue management methodology that has two innovative features. First, we develop a forecasting model that takes into account the correlation between the demand for different products and also the correlation between demand during different time periods. The EM algorithm is used to estimate the model, in order to incorporate censored demand data. Second, we show how to determine bid prices using an optimization algorithm based on a multistage stochastic program. Simulations studies and an application to a real airline booking dataset show the improved performance of our methodology.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2004 program

JSM 2004 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2004