Data-driven optimization with behavioral considerations : applications to pricing

This thesis aims to introduce descriptive and predictive models that guide more informed pricing strategies in practice, drawing from interdisciplinary work of current OM, behavioral theories and recent machine learning advances. In chapter 2, we integrate a consumer purchase experiment and an analytical model to investigate how consumers' price-based quality perception, expected markdown, and a product's availability information influence a retailer's markdown pricing strategy. We subsequently develop a consumer model that incorporates consumers' price-based quality perception observed from the experimental data and consumers' potential loss aversion. We embed this consumer model into the retailer's markdown optimization and examine the impact of these behavioral factors on the retailer's optimal strategy.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Hariss, Rim.

Advisor dc:contributor.advisor

• Georgia Perakis and Yanchong Zheng.

• Operations Research Center.

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

https://hdl.handle.net/1721.1/123707

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123707