Hotel revenue management: Benefits of simultaneous overbooking and allocation problem formulation in price optimization

Abstract We develop a hotel revenue management optimization method in an environment where market segment prices are optimized via demand curves ahead of a planning horizon. This new method simultaneously optimizes overbooking levels and allocation (of capacity to market segments) levels, as opposed to the traditional sequential approach. We test our method against the reference in a simulation of a hotel reservation system that has all the functionality of a real-world revenue management system: the estimation of true demand from censored demand; different market segments with different demand patterns; price elasticities; varying propensities to stay certain lengths of time; short- and long-term forecasting with periodic reoptimization of all forecaster parameters; explicit optimization of market segment prices based on estimated demand curves; and optimization routines for overbooking and allocation. A walkthrough of this simulation was performed by the revenue management staff at a major hotel. This simulation has been scaled down to permit extensive experimentation. Our new method outperforms the reference method by an average of 20.2% with respect to nightly net revenue. The improvement is much larger in situations where demand is more saturated. Our new method takes less than two minutes of computing time from a cold start on a realistically sized problem, which is sufficiently fast for hotel managers who want the capability of rerunning the algorithm many times during the course of a day.

[1]  Tim Baker,et al.  THE BENEFITS OF OPTIMIZING PRICES TO MANAGE DEMAND IN HOTEL REVENUE MANAGEMENT SYSTEMS , 2003 .

[2]  Dan Zhang,et al.  Revenue Management for Parallel Flights with Customer-Choice Behavior , 2005, Oper. Res..

[4]  Bingzhou Li.  A Cruise Line Dynamic Overbooking Model with Multiple Cabin Types from the View of Real Options , 2014 .

[5]  Huseyin Topaloglu,et al.  Revenue Management Under the Markov Chain Choice Model , 2017, Oper. Res..

[6]  Mark E. Ferguson,et al.  The "Killer Application" of Revenue Management: Harrah's Cherokee Casino & Hotel , 2008, Interfaces.

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

[8]  Matthias Gerdts,et al.  The oracle penalty method , 2010, J. Glob. Optim..

[9]  Shouhong Wang,et al.  A hybrid threshold curve model for optimal yield management: neural networks and dynamic programming , 2001 .

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

[11]  E. Andrew Boyd,et al.  Revenue Management and E-Commerce , 2003, Manag. Sci..

[12]  Garrett J. van Ryzin,et al.  A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management , 1997, Oper. Res..

[13]  Amy M. Gregory,et al.  An evaluation of essential revenue management competencies: similarities and differences between practitioners and educators , 2017 .

[14]  Peter Paul Belobaba,et al.  Air travel demand and airline seat inventory management , 1987 .

[15]  Francis de Véricourt,et al.  Resource and Revenue Management in Nonprofit Operations , 2009, Oper. Res..

[16]  Richard E. Chatwin,et al.  Continuous-Time Airline Overbooking with Time-Dependent Fares and Refunds , 1999, Transp. Sci..

[17]  Richard E. Chatwin.  Multi-period airline overbooking with multiple fare classes , 1996 .

[18]  Garrett J. van Ryzin,et al.  Technical Note - An Expectation-Maximization Method to Estimate a Rank-Based Choice Model of Demand , 2017, Oper. Res..

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

[20]  Jochen Gönsch,et al.  A Survey on Risk-Averse and Robust Revenue Management , 2017, Eur. J. Oper. Res..

[21]  Naragain Phumchusri,et al.  Two-dimensional air cargo overbooking models under stochastic booking request level, show-up rate and booking request density , 2016, Comput. Ind. Eng..

[22]  Craig V. Eister,et al.  Retail Price Optimization at InterContinental Hotels Group , 2012, Interfaces.

[23]  Abhijit Gosavi,et al.  Simulation optimization for revenue management of airlines with cancellations and overbooking , 2006, OR Spectr..

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

[25]  Stephen R. Lawrence,et al.  Clinic Overbooking to Improve Patient Access and Increase Provider Productivity , 2007, Decis. Sci..

[26]  Xiaolong Guo,et al.  Optimal pricing strategy for hotels when online travel agencies use customer cash backs: A game-theoretic approach , 2016 .

[27]  Alberto Garcia-Diaz,et al.  A polyhedral graph theory approach to revenue management in the airline industry , 2000 .

[28]  Jun Li,et al.  The booking problem of a diagnostic resource with multiple patient classes and emergency interruptions , 2017, Comput. Ind. Eng..

[29]  Mark E. Ferguson,et al.  Data Set - Choice-Based Revenue Management: Data from a Major Hotel Chain , 2009, Manuf. Serv. Oper. Manag..

[30]  Dimitris Bertsimas,et al.  Restaurant Revenue Management , 2003, Oper. Res..

[31]  Oli B.G. Madsen,et al.  Booking Control Increases Profit at Scandinavian Airlines , 1989 .

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

[33]  Alberto Garcia-Diaz,et al.  A cutting-plane procedure for maximizing revenues in yield management , 1997 .

[34]  Sebastian Koch,et al.  Dynamic Programming Decomposition for Choice-Based Revenue Management with Flexible Products , 2017 .

[35]  Barry C. Smith,et al.  Yield Management at American Airlines , 1992 .

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

[37]  Yifan Xu,et al.  Overbooking for parallel flights with transference , 2013 .

[38]  Ek Peng Chew,et al.  Joint inventory allocation and pricing decisions for perishable products , 2009 .

[39]  Changkyu Park,et al.  Seat inventory control for sequential multiple flights with customer choice behavior , 2011, Comput. Ind. Eng..

[40]  Raja G. Kasilingam.  An economic model for air cargo overbooking under stochastic capacity , 1997 .

[41]  Morris A. Cohen,et al.  Capacity Management in Rental Businesses with Two Customer Bases , 2005, Oper. Res..

[42]  K. Littlewood..  Special Issue Papers: Forecasting and control of passenger bookings , 2005 .

[43]  Itir Z. Karaesmen,et al.  Overbooking with Substitutable Inventory Classes , 2004, Oper. Res..

[44]  Pinar Keskinocak,et al.  Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions , 2003, IEEE Engineering Management Review.

[45]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[46]  Ronald E. Giachetti,et al.  A Stochastic Mathematical Appointment Overbooking Model for Healthcare Providers to Improve Profits , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[47]  Carol A. Ankenbrandt.  An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms , 1990, FOGA.

[48]  Kalyan T. Talluri,et al.  Tractable Consideration Set Structures for Assortment Optimization and Network Revenue Management , 2017 .

[49]  Julio R. Banga,et al.  Extended ant colony optimization for non-convex mixed integer nonlinear programming , 2009, Comput. Oper. Res..

[50]  Giovanna Miglionico,et al.  Revenue management policies for the truck rental industry , 2012 .

[51]  Lu Ji,et al.  Application of modified nested and dynamic class allocation models for cruise line revenue management , 2007 .

[52]  Catherine Cleophas,et al.  Resilient revenue management: a literature survey of recent theoretical advances , 2017 .