Approximation Schemes for the Joint Inventory Selection and Online Resource Allocation Problem

53 Pages Posted: 8 Nov 2021

See all articles by Xingxing Chen

Xingxing Chen

University of Richmond - Robins School of Business

Jacob Feldman

Washington University in St. Louis - John M. Olin Business School

Seung Hwan Jung

Yonsei University

Panos Kouvelis

Washington University in St. Louis

Date Written: November 4, 2021

Abstract

In this paper, we introduce and study the joint inventory and online resource allocation problem, which is characterized by two sequential sets of decisions that are irrevocably linked. First, a decision maker must select starting inventory levels for a set of available resources. Subsequently, the decision maker must match arriving customers to available resources in an online fashion so as to maximize expected reward. We fi rst study the problem in its most general form before focusing on a specific version that arises at Anheuser Busch InBev (ABI). This particular application of our general setting is referred to as the ABI Trailer Problem, and it considers how ABI ships its beer to vendors via third party delivery trucks. Specifically, in this problem, ABI must select the weights and inventory levels of preloaded trailers of beer, which are then matched in an online fashion to arriving third party delivery trucks. For the general setting, we develop simple and easy-to-implement approaches that come with robust worst-case performance guarantees. For the ABI setting, we reveal a simplifying structural property related to the optimal matching policy, which gives rise to a natural adaptation of our original approach. We test the efficacy of these policies through extensive numerical experiments, where we find that our approaches are either near-optimal or improve upon state-of-the-art benchmarks. In particular, using a data set from ABI, we are able to generate instances of the ABI Trailer Problem, on which our algorithm has the potential to yield revenue improvements in the range of millions of dollars per year.

Suggested Citation

Chen, Xingxing and Feldman, Jacob and Jung, Seung Hwan and Kouvelis, Panos, Approximation Schemes for the Joint Inventory Selection and Online Resource Allocation Problem (November 4, 2021). Available at SSRN: https://ssrn.com/abstract=3956503 or http://dx.doi.org/10.2139/ssrn.3956503

Xingxing Chen

University of Richmond - Robins School of Business ( email )

102 UR Drive
Richmond, VA 23173
United States

Jacob Feldman (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Seung Hwan Jung

Yonsei University ( email )

Seoul
Korea, Republic of (South Korea)

Panos Kouvelis

Washington University in St. Louis ( email )

One Brookings Drive
Campus Box 1156
St. Louis, MO 63130-4899
United States

HOME PAGE: http://www.panoskouvelis.info

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
85
Abstract Views
322
rank
399,438
PlumX Metrics