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<p>This paper investigates the vaccine distribution network that is applicable in emerging countries. We propose a two-stage stochastic programming model for the multi-period vaccine network design problem, taking into account uncertain demand, product perishability, flow of products between hubs, and capacity expansion. The model is expressed using a Mixed-Integer Linear Programming model. The primary goal is to minimize the total cost by optimizing the locations of the distribution centers, the optimal period to install storage devices, and the flow of vaccines in the network. The dynamic transmission compartment model (DCM) is used to generate more accurate stochastic demand scenarios in the face of epidemic disruptions. The data preparation algorithm to transform the DCM data into demand scenarios is developed using the <i>k</i>-means clustering method. The proposed two-stage stochastic programming model is solved using the Sample Average Approximation method. The proposed framework for the solution method is assessed using datasets inspired by Indonesia’s COVID-19 vaccination project. Computational experiments demonstrate that the proposed framework obtains good solutions that can be implemented by the relevant stakeholders.</p>

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Optimizing the vaccine distribution network design problem under uncertainty

  • 期刊论文

  • 发表日期：

    2026年4月8日

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  • Paulina Kus Ariningsih,
  • Chandra Ade Irawan,
  • Antony Paulraj,
  • Jing Dai

摘要

This paper investigates the vaccine distribution network that is applicable in emerging countries. We propose a two-stage stochastic programming model for the multi-period vaccine network design problem, taking into account uncertain demand, product perishability, flow of products between hubs, and capacity expansion. The model is expressed using a Mixed-Integer Linear Programming model. The primary goal is to minimize the total cost by optimizing the locations of the distribution centers, the optimal period to install storage devices, and the flow of vaccines in the network. The dynamic transmission compartment model (DCM) is used to generate more accurate stochastic demand scenarios in the face of epidemic disruptions. The data preparation algorithm to transform the DCM data into demand scenarios is developed using the k-means clustering method. The proposed two-stage stochastic programming model is solved using the Sample Average Approximation method. The proposed framework for the solution method is assessed using datasets inspired by Indonesia’s COVID-19 vaccination project. Computational experiments demonstrate that the proposed framework obtains good solutions that can be implemented by the relevant stakeholders.