Skip to main content

Master in Business Analytics Thesis Defense: Fatemeh Gholizadehfotouhaba



Fatemeh Gholizadehfotouhabadi

Master in Business Analytics Thesis Defense


Date: July 12th, 2021, Monday @ 4 pm


Zoom link:


Keywords: Preference learning, Recommendation sets, Bi-level optimization, Ride-sharing, Crowdsourced delivery




Peer-to-peer logistics platforms iteratively match free agents who are willing to offer services to the demand requests. In some settings, the platform may not have full information about the suppliers’ request preferences. To reduce this uncertainty, the platform can offer a menu of requests to each supplier. By collecting the supplier’s selected request in each decision period, the platform has access to the historical data of each supplier. The gathered information helps the platform learn the supplier’s utility function over time. We simulate a ride-sharing setting, iteratively generating menus of requests for each driver and using the drivers’ selection information in each period to learn the driver’s utility function and predict the driver’s future behavior. We show that as we learn the drivers’ utility function, offering too many options might cause the driver to deviate from the platform’s optimal request assignment therefore, it is beneficial to decrease the menu size as we learn.

Duyuru ve etkinliklerimizden haberdar olmak için abone olun