Correspondence: Soheil Sibdari, Charlton College of Business, University of Massachusetts, Dartmouth, MA 02747, USA. Tel: +1 508 999 8019; Fax: +1 508 999 8646; E-mail: ssibdari@umassd.edu

¹Soheil Sibdari is an assistant professor at the Charlton College of Business, University of Massachusetts Dartmouth. He received both his MS in Economics and PhD in Industrial Engineering from Virginia Tech. His research interests include dynamic pricing, revenue management, game theory applications, and transportation planning.

²Kyle Y. Lin is an associate professor at the Operations Research Department, Naval Postgraduate School. He received his MS and PhD in Industrial Engineering and Operations Research from UC Berkeley. His research interests include stochastic modelling, decision making under uncertainty, queueing theory, and game theory. He is an associate editor for the journal Operations Research.

³Sriram Chellappan received his MS in Transportation Engineering from University of Maryland College Park. He is the director of decision support system at National Railroad Corporation (Amtrak). In this position he is responsible for setting strategy and goal for gathering and disseminating Business Intelligence along with the implementation of analytical models and tools.

Received 25 July 2007; Revised 25 July 2007; Published online 29 February 2008.

Abstract

This study involves working with Amtrak, the National Railroad Passenger Corporation, to develop a revenue management model. The Revenue Management Department at Amtrak provides the sales data of Auto Train, a service of Amtrak that allows passengers to bring their vehicles on the train. We analysed the demand from the sales data and built a mathematical model to develop a pricing system for Auto Train. An algorithm was developed to calculate the optimal pricing strategy that yields the maximum revenue. We further introduced three pricing policies Myopic policy, Static-Price heuristic, and Pseudo-Dynamic heuristics, as benchmarks for our dynamic programming solution. Because Auto Train is a real-world application of multiproduct revenue management, our findings make an important contribution to the revenue management literature.