We investigate the revenue impact of a new Price Setting Method (PSM) and compare it with the industry standard Bid Price Method (BPM). This comparison is performed via a simulation that was validated by a major hotel chain. In 27 out of the 32 cases, the PSM outperformed the BPM based on statistically significant tests. The PSM produces an average revenue increase of 34%, which can be thought of as an upper bound on the realistic revenue increase.
Alliance (1998), Simulation review with The Alliance Group, Chicago, Illinois. [This group maintains Hyatt Hotel's revenue management systems software.].
2.
BakerT. and CollierD. (1999), “A Comparative Revenue Analysis of Hotel Yield Management Heuristics”, Decision Sciences, 30, 1, 239–263.
3.
BelobabaP. and FarkasA. (1999), “Yield Management Impacts on Airline Spill Estimation”, Transportation Science, 33, 2, 217–232.
4.
BinderA. (1991), “Why are Prices Sticky? Preliminary Results from an Interview Study”, The American Economic Review, 81, 2, 89–96.
5.
BitranG. and MondscheinV. (1995), “An Application of Yield Management to the Hotel Industry Considering Multiple Day Stays”, Operations Research, 43, 3, 427–443.
6.
CarltonD. (1977), “Uncertainty, Production Lags, and Pricing”, The American Economic Review, 67, 1, 244–249.
7.
ChatwinR. (1999), “Continuous‐Time Airline Overbooking with Time‐Dependent Fares and Refunds”, Transportation Science, 33, 2, 182–191.
8.
CianciminoA.InzerilloG.LucidiS., and PalagiL. (1999), “A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem”, Transportation Science, 33, 2, 168–181.
9.
CollierD. A. (1994), The Service/Quality Solution: Using Service Management to Gain Competitive Advantage. Co‐published by Quality Press, Milwaukee, Wisconsin and Richard D. Irwin, Inc., Burr Ridge, Illinois.
10.
CrossR. G. (1997), Revenue Management: Hard‐Core Tactics for Market Domination, Broadway Books, New York.
11.
FurnissM. A. (2002), Simulation review with the Revenue Management Systems Group at Six Continents Hotels (formerly Bass Hotels and Resorts), Atlanta, Georgia.
12.
GallegoG. and Van RyzinG. (1997), “A Multiproduct Dynamic Pricing Problem and Its Application to Network Yield Management”, Operations Research, 45, 1, 24–41.
13.
GeraghtyM. and JohnsonM. (1997), “Revenue Management Saves National Car Rental”, Interfaces, 12, 7, 107–127.
14.
HanksR. D.CrossR. G., and NolandR. P. (1992), “Discounting in the Hotel Industry: A New Approach”, Cornell Hotel and Restaurant Administration Quarterly, 33, 1, 15–23.
15.
IMSL C Mathematical/Statistical Library (1999), Version 3.0, Visual Numerics Corporation, Houston, Texas.
16.
KimesS. (2000), “Revenue Management on the Links: Applying Yield Management to the Golf‐Course Industry”, Cornell Hotel and Restaurant Administration Quarterly, 41, 1, 120–127.
17.
LiangY. (1999), “Solution to the Continuous Time Dynamic Yield Management Model”, Transportation Science, 33, 1, 117–123.
18.
LiebermanW. (1992), “Implementing Yield Management”, ORSA/TIMS National Meeting Presentation, November, San Francisco, California.
19.
MaceA. (1997), Discussion with Andrew Mace, a vice president at Manugistics (formerly Talus, Inc).
20.
MettersR. and VargasV. (1999), “Yield Management for the Nonprofit Sector”, Journal of Service Research, 1, 3, 215–226.
21.
NairS. and BapnaR. (2001), “An Application of Yield Management for Internet Service Providers”, Naval Research Logistics, 48, 5, 348–362.
22.
PfeifferP. (1989), “The Airline Discount Fare Allocation Problem”, Decision Sciences, 20, 1, 149–157.
23.
SmithB. C.LeimkuhlerJ. F., and DarrowR. M. (1992), “Yield Management at American Airlines”, Interfaces, 22, 1, 8–31.
24.
StewartD. (1999), Discussions with Doug Stewart, a revenue management analyst with Manugistics, Inc.
25.
TalluriK. and Van RyzinG. (1998), “An Analysis of Bid‐Price Controls for Network Revenue Management”, Management Science, 44, 11, Part 1 of 2, 1577–1593.
26.
TalluriK. and Van RyzinG. (1999), “A Randomized Linear Programming Method for Computing Network Bid Prices”, Transportation Science, 33, 2, 207–216.
27.
Van SlykeR. and YoungY. (2000), “Finite Stochastic Knapsacks with Applications to Yield Management”, Operations Research, 48, 1, 155–172.
28.
VollmanT.BerryW., and WhybarkD. (1988), Manufacturing Planning and Control Systems, Irwin.
29.
WeatherfordL. (1995), “Length‐of‐Stay Heuristics: Do They Really Make a Difference”, Cornell Hotel and Restaurant Administration Quarterly, 36, 6, 70–79.
30.
WeatherfordL. and BelobabaP. (2002), “Revenue Impacts of Fare Input and Demand Forecast Accuracy in Airline Yield Management”, Journal of the Operational Research Society, 53, 8, 811–821.
31.
WeatherfordL. and BodilyS. (1992), “A Taxonomy and Research Overview of Perishable‐Asset Revenue Management: Yield Management, Overbooking, and Pricing”, Operations Research, 40, 5, 831–844.
32.
WeatherfordL.KimesS., and ScottD. (2001), “Forecasting for Hotel Revenue Management”, Cornell Hotel and Restaurant and Administration Quarterly, 42, 4, August, 53–64.
33.
YouP. (1999), “Dynamic Pricing in Airline Seat Management for Flights with Multiple Flight Legs”, Transportation Science, 33, 2, 192–206.
34.
ZhaoW. and ZhengY. (2000), “Optimal Dynamic Pricing for Perishable Assets with Nonhomogenous Demand”, Management Science, 46, 3, 375–388.
