In this paper, we consider the yet-uncharted assortment optimization problem under the Exponomial choice model, where the objective is to determine the revenue maximizing set of products that should be offered to customers. Our main algorithmic contribution comes in the form of a fully...

We introduce the click-based MNL choice model, a novel framework for capturing customer purchasing decisions in e-commerce settings. We augment the classical Multinomial Logit choice model with the assumption that customers only consider the items they have clicked on before they proceed to...

We study a multi-period, multi-product, dynamic pricing problem with price adjustment costs known as menu costs. In this setting, we assume that decision makers (e.g., retailers, online platforms) incur a fixed menu cost whenever prices are updated between periods, as well as a variable menu...

In this paper, we propose a new method to compute lower bounds on the optimal objective value of a stochastic program and show how this method can be used to construct separable approximations to the recourse functions. We show that our method yields tighter lower bounds than Jensen's lower...