Near-Optimal Bayesian Online Assortment of Reusable Resources

TL;DR

This paper develops near-optimal online algorithms for revenue maximization in the assortment of reusable resources, using Bayesian models and LP-based randomized rounding, with technical innovations in inventory feasibility and resource management.

Contribution

It introduces a novel LP-based randomized rounding approach with discard policies and post-processing for reusable resources, achieving near-optimal competitive ratios.

Findings

Achieves a near-optimal 1 - min(1/2, sqrt(log(c_0)/c_0)) competitive ratio.

Provides an improved 1 - 1/sqrt(c_0+3) ratio for non-reusable resources.

Demonstrates effectiveness through numerical simulations on synthetic data.

Abstract

Motivated by the applications of rental services in e-commerce, we consider revenue maximization in online assortment of reusable resources for a stream of arriving consumers with different types. We design competitive online algorithms with respect to the optimum online policy in the Bayesian setting, in which types are drawn independently from known heterogeneous distributions over time. In the regime where the minimum of initial inventories $c_0$ is large, our main result is a near-optimal $1-\min\left(\frac{1}{2},\sqrt{\log(c_0)/c_0}\right)$ competitive algorithm for the general case of reusable resources. Our algorithm relies on an expected LP benchmark for the problem, solves this LP, and simulates the solution through an independent randomized rounding. The main challenge is obtaining point-wise inventory feasibility in a computationally efficient fashion from these simulation-based algorithms. To this end, we use several technical ingredients to design $\textit{discarding policies}$ -- one for each resource. These policies handle the trade-off between the inventory feasibility under reusability and the revenue loss of each of the resources. However, discarding a unit of a resource changes the future consumption of other resources. To handle this new challenge, we also introduce $\textit{post-processing}$ assortment procedures that help with designing and analyzing our discarding policies as they run in parallel, which might be of independent interest. As a side result, by leveraging techniques from the literature on prophet inequality, we further show an improved near-optimal $1-1/\sqrt{c_0+3}$ competitive algorithm for the special case of non-reusable resources. We finally evaluate the performance of our algorithms using the numerical simulations on the synthetic data.