We study an adversarial online assortment problem with reusable resources and customer-dependent usage durations. We propose a novel online algorithm which features rejection durations filtering out unprofitable products. We show that it achieves a competitive ratio within a constant factor from the best possible one with large capacities. This is the first work that derives a non-trivial performance guarantee for such problem. We further extend our algorithm framework to other reward functions.

We consider an assortment optimization problem for a class of online video games. Our paper is the first to study assortment optimization for the gaming industry under discrete choice models; it is also the first to devise solution approaches for the constrained mixture-of-nested-logit model with performance guarantees. Numerical experiments show that our approaches perform well across a variety of settings. Our work provides guidance to online video game stores for effective revenue maximization.

We study a new class of stock-dependent pricing policies for reusable resources that set prices based on the stock at hand. To find the optimal policy in this class, we introduce a reformulation that is convex. We provide a sharp characterization of the regret of this policy class via matching upper and lower bounds and show they can significantly improve upon static pricing. A simple two-price policy that changes prices when the stock is below a threshold can achieve the optimal rate of regret.