JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Mathematical Model for Revenue Management with Overbooking and Costly Price Adjustment for Hotel Industries
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Mathematical Model for Revenue Management with Overbooking and Costly Price Adjustment for Hotel Industries
Masruroh, Nur Aini; Mulyani, Yun Prihantina;
  PDF(new window)
 Abstract
Revenue management (RM) has been widely used to model products characterized as perishable. Classical RM model assumed that price is the sole factor in the model. Thus price adjustment becomes a crucial and costly factor in business. In this paper, an optimal pricing model is developed based on minimization of soft customer cost, one kind of price adjustment cost and is solved by Lagrange multiplier method. It is formed by expected discounted revenue/bid price integrating quantity-based RM and pricing-based RM. Quantity-based RM consists of two capacity models, namely, booking limit and overbooking. Booking limit, built by assuming uncertain customer arrival, decides the optimal capacity allocation for two market segments. Overbooking determines the level of accepted order exceeding capacity to anticipate probability of cancellation. Furthermore, pricing-based RM models occupancy/demand rate influenced by internal and competitor price changes. In this paper, a mathematical model based on game theoretic approach is developed for two conditions of deterministic and stochastic demand. Based on the equilibrium point, the best strategy for both hotels can be determined.
 Keywords
Revenue Management;Costly Price Adjustment;Overbooking;Game Theory;Cancellation;Capacity Allocation;
 Language
English
 Cited by
 References
1.
Bertsimas, D. and De Boer, S. (2005), Simulation-based booking limits for airline revenue management, Operations Research, 53(1), 90-106. crossref(new window)

2.
Bitran, G. B. and Gilbert, S. M. (1996), Managing hotel reservation with uncertain arrivals, Operations Research, 44(1), 35-49. crossref(new window)

3.
Chen, X., Zhou, S. X., and Chen, Y. (2010), Integration of inventory and pricing decisions with costly price adjustments, Operations Research, 59(5), 1144-1158.

4.
Dolgui, A. and Porth, J. (2010), Pricing strategy and models, Annual Reviews in Control, 34(1), 101-110. crossref(new window)

5.
Dai, Y., Chao, X., Fang, S. C., and Nuttle, H. L. W. (2004), Pricing in revenue management for multiple firms competing for customers, International Journal Production Economics, 98(1), 1-16.

6.
Gothesson, L. and Riman, S. (2004), Revenue manage ment within Swedish hotels, M.S. thesis, School of Economics and Commercial Law, Goteborg University, Goteborg, Sweden.

7.
Netessine, S. and Shumsky, R. A. (2004), Revenue management games: horizontal and vertical competition, Management Science, 51(5), 813-831.

8.
Netessine, S., Shumky, R. A., and Sadagopan, N. (2002), Introduction to the theory and practice of yield management, INFORMS Transactions on Education, 3(1), 34-44. crossref(new window)

9.
Rapoport, A. (1971), Three- and four-person games, Comparative Group Studies, 2(2), 191-226. crossref(new window)

10.
Talluri, K. T. and Van Ryzin, G. J. (2004), The Theory and Practice of Revenue Management, Kluwer Academic Publishers, Boston, MA.

11.
Turocy, T. L. and Von Stengel, B. (2001), Game theory, CDAM Research Report LSE-CDAM-2001-09, Mathematics Department, London School of Economics, London, UK.

12.
Wolman, A. L. (2000), The frequency and costs of individual price adjustment, Federal Reserve Bank of Richmond Economic Quarterly, 86(4), 1-22.