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A STOCHASTIC APPROACH TO HOTEL REVENUE MANAGEMENT CONSIDERING MULTIPLE-DAY STAYS

SHUQIN LIU

School of Economics and Management, Beihang University, Beijing 100083, China

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KIN KEUNG LAI

Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue Kowloon, Hong Kong, China

College of Business Administration, Hunan University, Hunan 410082, China

Corresponding author.

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JICHANG DONG

School of Management, Graduate School, Chinese Academy of Sciences, Beijing 100080, China

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, and

SHOU-YANG WANG

Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China

Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan

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https://doi.org/10.1142/S021962200600212XCited by:8 (Source: Crossref)

Abstract

We present a stochastic optimization model for hotel revenue management with multiple-day stays under an uncertain environment. Since a decision maker may face several scenarios when renting out rooms, we use a semi-absolute deviation model to measure the risk of hotel revenue, and only consider the risk of falling below the expected revenue. The method proposed in this paper can be changed to a linear programming model by applying linearization techniques. Some examples are presented to illustrate the efficiency of this method.

Keywords:

References

R. D. Badinelli, European Journal of Operational Research 121, 476 (2000), DOI: 10.1016/S0377-2217(99)00046-6. Crossref, Web of Science, Google Scholar
T. K. Baker and D. A. Collier, Decision Sciences 30(1), 239 (1999). Crossref, Web of Science, Google Scholar
G. Bitran and S. V. Mondschein, Operations Research 43(3), 427 (1995). Crossref, Web of Science, Google Scholar
S. V. Boer, R. Freling and N. Piersma, European Journal of Operational Research 137, 72 (2002), DOI: 10.1016/S0377-2217(01)00096-0. Crossref, Web of Science, Google Scholar
E. A. Boyd and I. C. Bilegan, Management Science 49(10), 1363 (2003), DOI: 10.1287/mnsc.49.10.1363.17316. Crossref, Web of Science, Google Scholar
S. Brumelle and D. Walczak, Operations Research 51(1), 137 (2003), DOI: 10.1287/opre.51.1.137.12796. Crossref, Web of Science, Google Scholar
P. Goldmanet al., Journal of Revenue and Pricing Management 1(3), 207 (2002). Crossref, Web of Science, Google Scholar
R. D. Hanks, R. G. Cross and R. P. Noland, Cornell Hotel and Restaurant Administration Quarterly 33(2), 40 (1992). Google Scholar
S. E. Kimes, Journal of Operations Management 8(4), 348 (1989), DOI: 10.1016/0272-6963(89)90035-1. Crossref, Web of Science, Google Scholar
S. Ladany, Decision Sciences 7, 829 (1976). Crossref, Google Scholar
K. K. Lai and W. L. Ng, Computers and Operations Research 32(5), 1059 (2005). Crossref, Web of Science, Google Scholar
E. Orkin , How does yield management maximize profits for the hotel industry? , Yield Management Multi-Industry Conference ( 1989 ) . Google Scholar
M. Rajopadhyeet al., Information Sciences 132, 1 (2001), DOI: 10.1016/S0020-0255(00)00082-7. Crossref, Web of Science, Google Scholar
W. J. Relihan, Cornell Hotel and Restaurant Administration Quarterly 30(3), 40 (1989). Crossref, Google Scholar
M. Rothstein, Decision Science 5, 389 (1974). Crossref, Google Scholar
L. R. Weatherford and S. E. Bodily, Operations Research 10(5), 831 (1992). Google Scholar
L. R. Weatherford, Cornell Hotel and Restaurant Administration Quarterly 36(6), 70 (1995), DOI: 10.1016/0010-8804(96)81006-8. Crossref, Google Scholar
L. R. Weatherford and S. E. Kimes, International Journal of Forecasting 19, 401 (2003), DOI: 10.1016/S0169-2070(02)00011-0. Crossref, Web of Science, Google Scholar