A new approach to O&D revenue management based on scenario trees

Origin and destination (O&D) revenue management (RM), either leg-based or PNR based, has become a standard in the airline industry. This paper presents a new approach to O&D RM which does not make any assumptions on demand distributions or on the correlations of the booking process. Protection levels are determined for all origin—destination itineraries, fare classes, points of sale and data collection points (DCPs), and for a variety of demand patterns over the complete booking period. This approach to the seat inventory problem is modelled as a multistage stochastic program, where its stages correspond to the DCPs of the booking horizon. The stochastic passenger demand process is approximated by a scenario tree generated from historical data by a recursive scenario reduction procedure. The stochastic program represents a specially structured large scale linear program (LP) that may be solved by standard LP software (eg CPLEX). Preliminary numerical experience is reported.

[1]  F. Glover,et al.  The Passenger-Mix Problem in the Scheduled Airlines , 1982 .

[2]  Peter P. Belobaba,et al.  Survey Paper - Airline Yield Management An Overview of Seat Inventory Control , 1987, Transp. Sci..

[3]  B C Smith,et al.  ANALYSIS OF ALTERNATE ORIGIN-DESTINATION CONTROL STRATEGIES , 1988 .

[4]  Peter Belobaba,et al.  OR Practice - Application of a Probabilistic Decision Model to Airline Seat Inventory Control , 1989, Oper. Res..

[5]  Jeffrey I. McGill,et al.  Allocation of Airline Seats between Stochastically Dependent Demands , 1990, Transp. Sci..

[6]  Renwick E. Curry,et al.  Optimal Airline Seat Allocation with Fare Classes Nested by Origins and Destinations , 1990, Transp. Sci..

[7]  Elizabeth Louise Williamson,et al.  Airline network seat inventory control : methodologies and revenue impacts , 1992 .

[8]  Richard D. Wollmer,et al.  An Airline Seat Management Model for a Single Leg Route When Lower Fare Classes Book First , 1992, Oper. Res..

[9]  Jeffrey I. McGill,et al.  Airline Seat Allocation with Multiple Nested Fare Classes , 1993, Oper. Res..

[10]  Larry Weatherford.  A Tutorial on Optimization in the Context of Perishable-Asset Revenue Management Problems for the Airline Industry , 1998 .

[11]  K. Talluri,et al.  An Analysis of Bid-Price Controls for Network Revenue Management , 1998 .

[12]  S. Rachev,et al.  Mass transportation problems , 1998 .

[13]  Jeffrey I. McGill,et al.  Revenue Management: Research Overview and Prospects , 1999, Transp. Sci..

[14]  J. Dupacová,et al.  Scenario reduction in stochastic programming: An approach using probability metrics , 2000 .

[15]  Jitka Dupacová,et al.  Scenarios for Multistage Stochastic Programs , 2000, Ann. Oper. Res..

[16]  G. Ryzin,et al.  Revenue Management Without Forecasting or Optimization: An Adaptive Algorithm for Determining Airline Seat Protection Levels , 2000 .

[17]  Stein W. Wallace,et al.  Generating Scenario Trees for Multistage Decision Problems , 2001, Manag. Sci..

[18]  Georg Ch. Pflug,et al.  Scenario tree generation for multiperiod financial optimization by optimal discretization , 2001, Math. Program..

[19]  R. Schultz,et al.  Multistage Stochastic Integer Programs: An Introduction , 2001 .

[20]  Nanda Piersma,et al.  Airline revenue management: an overview of OR techniques 1982-2001 , 2002 .

[21]  Werner Römisch,et al.  Scenario Reduction Algorithms in Stochastic Programming , 2003, Comput. Optim. Appl..

[22]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[23]  Robert Klein,et al.  Revenue Management : eine weitere Erfolgsstory des Operations Research , 2003 .

[24]  William L. Cooper.  A Class of Hybrid Methods for Revenue Management , 2003 .

[25]  W. Lieberman.  The Theory and Practice of Revenue Management , 2005 .

[26]  W. Römisch,et al.  Scenario tree modelling for multistage stochastic programs , 2006 .