Online Allocation of Reusable Resources via Algorithms Guided by Fluid Approximations

by   Vineet Goyal, et al.

We consider the problem of online allocation (matching and assortments) of reusable resources where customers arrive sequentially in an adversarial fashion and allocated resources are used or rented for a stochastic duration that is drawn independently from known distributions. Focusing on the case of large inventory, we give an algorithm that is (1-1/e) competitive for general usage distributions. At the heart of our result is the notion of a relaxed online algorithm that is only subjected to fluid approximations of the stochastic elements in the problem. The output of this algorithm serves as a guide for the final algorithm. This leads to a principled approach for seamlessly addressing stochastic elements (such as reusability, customer choice, and combinations thereof) in online resource allocation problems, that may be useful more broadly.



There are no comments yet.


page 1

page 2

page 3

page 4


Online Allocation of Reusable Resources: Achieving Optimal Competitive Ratio

We study the problem of allocating a given set of resources to sequentia...

Online Resource Allocation under Partially Predictable Demand

For online resource allocation problems, we propose a new demand arrival...

Function Design for Improved Competitive Ratio in Online Resource Allocation with Procurement Costs

We study the problem of online resource allocation, where multiple custo...

Pricing for Online Resource Allocation: Beyond Subadditive Values

We consider the problem of truthful online resource allocation to maximi...

Online Learning and Matching for Resource Allocation Problems

In order for an e-commerce platform to maximize its revenue, it must rec...

Inventory Balancing with Online Learning

We study a general problem of allocating limited resources to heterogene...

Competitive Online Algorithms for Resource Allocation over the Positive Semidefinite Cone

We consider a new and general online resource allocation problem, where ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.