Resource Distribution Under Spatiotemporal Uncertainty of Disease Spread: Stochastic versus Robust Approaches

31 Pages Posted: 18 Mar 2021 Last revised: 8 Nov 2021

See all articles by Beste Basciftci

Beste Basciftci

Sabanci University

Xian Yu

University of Michigan at Ann Arbor

Siqian Shen

University of Michigan at Ann Arbor - Department of Industrial and Operations Engineering

Date Written: March 7, 2021

Abstract

Speeding up testing and vaccination is essential for controlling the coronavirus disease 2019 (COVID-19) pandemic. We develop mathematical frameworks for optimizing locations of distribution centers DCs and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease infections and demand for the resources. We aim to balance operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits in Michigan, to compare SP and DRO with a deterministic model using demand estimates and with the current resource distribution implemented in the real world. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the ``demand-driven'' properties of the SP and DRO solutions.

Note: Funding Statement: The authors gratefully acknowledge the partial support from U.S. National Science Foundation (NSF) grant #CMMI-1727618 and Department of Energy (DoE) grant #DE-SC0018018.

Declaration of Interests: None of the authors have competing interests.

Keywords: COVID-19 pandemic; vaccine distribution; resource allocation; stochastic integer programming; distributionally robust optimization; multi-objective optimization

Suggested Citation

Basciftci, Beste and Yu, Xian and Shen, Siqian, Resource Distribution Under Spatiotemporal Uncertainty of Disease Spread: Stochastic versus Robust Approaches (March 7, 2021). Available at SSRN: https://ssrn.com/abstract=3799367 or http://dx.doi.org/10.2139/ssrn.3799367

Beste Basciftci

Sabanci University ( email )

School of Management
Orhanli Tuzla
İstanbul, 34956
Turkey

Xian Yu

University of Michigan at Ann Arbor ( email )

500 S. State Street
Ann Arbor, MI 48109
United States

Siqian Shen (Contact Author)

University of Michigan at Ann Arbor - Department of Industrial and Operations Engineering ( email )

1205 Beal Avenue
Ann Arbor, MI 48109
United States

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