Analytics for hotels : demand prediction and decision optimization

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Sun, Rui, S.M. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• David Simchi-Levi.

• Civil and Environmental Engineering.
• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/111438

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111438