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David Simchi-Levi. Candela Garza, Eduardo Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2018-02-08T15:57:31Z 2018-02-08T15:57:31Z 2017 2017 http://hdl.handle.net/1721.1/113433 1020067717 Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 53-55). We present our work with a hotel company as an example of how machine learning techniques can be used to improve the demand predictions of a hotel property, as well as its pricing and capacity allocation decisions. First, we build a price-sensitive random forest model to predict the number of daily bookings for each customer market segment. We feed these predictions into a mixed integer linear program (MILP) to optimize prices and capacity allocations at the same time. We prove that the MILP can be equivalently solved as a linear program, and then show that it produces upper and lower bounds for the expected revenue maximization Dynamic Program (DP), and that the gap between the bounds depends on the probabilistic distribution of the demand. Thus, for high prediction accuracies, the optimal value of the DP can be closely approximated by the MILP solution. Finally, numerical results show that the optimized decisions are able to generate an increase in revenue compared to the historical policies, and that the fast running time achieved permits real time policy updates. by Eduardo Candela Garza. S.M. 55 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="55d9b84e-27a8-4df7-a944-342bc69c6717"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Candela Garza, Eduardo</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>We present our work with a hotel company as an example of how machine learning techniques can be used to improve the demand predictions of a hotel property, as well as its pricing and capacity allocation decisions. First, we build a price-sensitive random forest model to predict the number of daily bookings for each customer market segment. We feed these predictions into a mixed integer linear program (MILP) to optimize prices and capacity allocations at the same time. We prove that the MILP can be equivalently solved as a linear program, and then show that it produces upper and lower bounds for the expected revenue maximization Dynamic Program (DP), and that the gap between the bounds depends on the probabilistic distribution of the demand. Thus, for high prediction accuracies, the optimal value of the DP can be closely approximated by the MILP solution. Finally, numerical results show that the optimized decisions are able to generate an increase in revenue compared to the historical policies, and that the fast running time achieved permits real time policy updates.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>