Online Stochastic Allocation of Reusable Resources

TL;DR

This paper presents a new model and algorithms for allocating reusable resources to sequentially arriving heterogeneous customers, maximizing rewards under uncertainty and capacity constraints, with near-optimal performance when durations are short.

Contribution

The paper introduces a multi-objective stochastic allocation model for reusable resources and develops algorithms that achieve near-optimal rewards under certain conditions.

Findings

Policy achieves $1-O(\epsilon)$ of optimal rewards for small durations.

Performance improves as resource capacities increase.

Algorithms effectively handle model uncertainty and multiple reward objectives.

Abstract

We study a multi-objective model on the allocation of reusable resources under model uncertainty. Heterogeneous customers arrive sequentially according to a latent stochastic process, request for certain amounts of resources, and occupy them for random durations of time. The decision maker's goal is to simultaneously maximize multiple types of rewards generated by the customers, while satisfying the resource capacity constraints in each time step. We develop models and algorithms for deciding on the allocation actions. We show that when the usage duration is relatively small compared with the length of the planning horizon, our policy achieves $1-O(\epsilon)$ fraction of the optimal expected rewards, where $\epsilon$ decays to zero at a near optimal rate as the resource capacities grow.