This paper proposes a multiple-choice rank list model to extend the classic discrete choice model by allowing customers to choose multiple distinct alternatives. In addition, we propose a framework to extract customers' preferences from the clickstream data. The corresponding assortment optimization and personalization problems can be solved by mixed-integer linear programs. Numerical experiments based on Cainiao Network showcase the predictive power of the proposed model.

We propose and analyze a sequential design of price experimentation that balances the learning and earning trade-off in revenue management. Assuming the demand function belongs to a parametric family with an unknown parameter value, we derive a closed-form stopping rule based on the observed Fisher information. The decision maker adaptively stops learning and optimizes a price based on the cumulative information and there is no need to find an optimal “fixed” sample size a priori.

A broker matches suppliers with a single buyer in an industrial market by submitting bids to procure goods to the suppliers. After bids are evaluated, the broker learns the quantities procured and ships the goods to the buyer to meet its demand. Modeling the broker’s problem as a new type of newsvendor network problem, we study the effect of problem parameters and uncertainty on the optimal bids as well as conditions under which it is optimal for the broker to bid at multiple supply locations.

Ridesharing platforms have traditionally implemented dynamic walking, which asks passengers to walk a little towards the car in order to achieve more efficient matches. Using novel models and extremely detailed Lyft data, we propose the new paradigm of static walking, which communicates a predetermined pickup location to the rider.

Discovery program is an affirmative action policy used by NYC Department of Education to increase the number of disadvantaged students at specialized high schools. We show that the discovery program suffer many drawbacks both in practice and in theory, and explore possible replacements. We propose a minimal yet powerful modification of the current implementation via the joint seat allocation mechanism, which we show would improve the welfare of disadvantaged students maximally.

In this paper, we study how schools work around insufficient government funding with supplemental resources from nonprofits. We ask: (i) How do resource-supplementing workarounds differ across schools with different socioeconomic advantage? (ii) What policies can ensure workarounds do not exacerbate educational inequities? We answer thee questions by applying Little's Law with validation from 62 interviews from six strategically sampled schools with different levels of socioeconomic advantage.