Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

Basciftci, Beste; Yu, Xian; Shen, Siqian

doi:10.1016/j.cor.2022.106028

Title: Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

Abstract

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the 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, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. 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. Furthermore, our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictionsmore »« less

Authors:

^[1]; Yu, Xian ^[2];

^[3]

University of Iowa, Iowa City, IA (United States)
The Ohio State Univ., Columbus, OH (United States)
University of Michigan, Ann Arbor, MI (United States)

Publication Date:: Tue Sep 20 00:00:00 EDT 2022

Research Org.:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Org.:: USDOE; U.S. National Science Foundation (NSF)

OSTI Identifier:: 1995051

Alternate Identifier(s):: OSTI ID: 1892124

Grant/Contract Number:: SC0018018; CMMI-1727618; 2041745; -SC0018018

Resource Type:: Accepted Manuscript

Journal Name:: Computers and Operations Research

Additional Journal Information:: Journal Volume: 149; Related Information: Beste Basciftci, Xian Yu, Siqian Shen, “Resource Distribution Under Spatiotemporal Uncertainty of Disease Spread: Stochastic versus Robust Approaches,” Computers and Operations Research, volume 149, 2023.; Journal ID: ISSN 0305-0548

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; COVID-19 pandemic; Vaccine distribution; Resource allocation; Stochastic integer programming; Distributionally robust optimization; Multi-objective optimization

Citation Formats


                    Basciftci, Beste, Yu, Xian, and Shen, Siqian. Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches.  United States: N. p., 2022. 
Web.  doi:10.1016/j.cor.2022.106028.

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                    Basciftci, Beste, Yu, Xian, & Shen, Siqian. Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches.  United States.  https://doi.org/10.1016/j.cor.2022.106028

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                    Basciftci, Beste, Yu, Xian, and Shen, Siqian. Tue .  
"Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches".  United States.  https://doi.org/10.1016/j.cor.2022.106028.  https://www.osti.gov/servlets/purl/1995051.

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@article{osti_1995051,

  title        = {Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches},

  author       = {Basciftci, Beste and Yu, Xian and Shen, Siqian},

  abstractNote = {We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the 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, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. 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. Furthermore, our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.},

  doi          = {10.1016/j.cor.2022.106028},

  journal      = {Computers and Operations Research},

  number       = ,

  volume       = 149,

  place        = {United States},

  year         = {Tue Sep 20 00:00:00 EDT 2022},

  month        = {Tue Sep 20 00:00:00 EDT 2022}

}

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Title: Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

Abstract

Citation Formats

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