Analytics for hotels : demand prediction and decision optimization
Name
1003292735-MIT.pdf
Description
Full printable version
Size
7.87 MB
Format
Adobe PDF
Checksum (MD5)
0a4299c7506b6d6ef3c4daf0b1ca7050
Author(s)
Sun, Rui, S.M. Massachusetts Institute of Technology
Advisor(s)
David Simchi-Levi.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
The thesis presents the work with a hotel company, as an example of how machine learning techniques can be applied to improve demand predictions and help a hotel property to make better decisions on its pricing and capacity allocation strategies. To solve the decision optimization problem, we first build a random forest model to predict demand under given prices, and then plug the predictions into a mixed integer program to optimize the prices and capacity allocation decisions. We present in the numerical results that our demand forecast model can provide accurate demand predictions, and with optimized decisions, the hotel is able to obtain a significant increase in revenue compared to its historical policies.
Description
Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 69-71).
Subjects
Civil and Environmental Engineering.
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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