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Locate vaccination stations considering travel distance, operational cost, and work schedule

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  • Li, Xin
  • Pan, Yanchun
  • Jiang, Shiqiang
  • Huang, Qiang
  • Chen, Zhimin
  • Zhang, Mingxia
  • Zhang, Zuoyao

Abstract

In this study, a multi-objective mixed-integer nonlinear programming (MINLP) model is proposed to help the Center for Disease Control and Prevention (CDC) determine the locations of vaccination stations while, simultaneously, considering travel distance, operational cost, and work schedule. A case based on Nanshan CDC in Shenzhen City of China is also studied. Due to the computational complexity, a two-stage strategy is developed to simplify the MINLP into a mixed-integer linear programming (MILP) model, and then ε-constraint method is applied to handle the multiple conflicting objectives. Our research reveals interesting and valuable results. For a given bound of operational cost, the number of fully open stations can greatly increase by wisely allocating demand groups and medical professionals. Although bearing more cost is in favor of decreasing travel distance, it is not beneficial to invest too much due to the decreasing effect, especially when requiring a high number of fully open stations. Current service capacity is large enough even if the birth rate increases by 50%, regarding the “two-child” policy. The case study validates that the proposed model can provide good Pareto non-inferior solutions for the CDC to balance different objectives and show great potential in locating public healthcare resources.

Suggested Citation

  • Li, Xin & Pan, Yanchun & Jiang, Shiqiang & Huang, Qiang & Chen, Zhimin & Zhang, Mingxia & Zhang, Zuoyao, 2021. "Locate vaccination stations considering travel distance, operational cost, and work schedule," Omega, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:jomega:v:101:y:2021:i:c:s0305048319310011
    DOI: 10.1016/j.omega.2020.102236
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    2. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
    3. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    4. Kamran, Mehdi A. & Kia, Reza & Goodarzian, Fariba & Ghasemi, Peiman, 2023. "A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    5. Wang, Xin & Jiang, Ruiwei & Qi, Mingyao, 2023. "A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply," Omega, Elsevier, vol. 119(C).
    6. Chen, Weiwei & Kumcu, Gül Çulhan & Melamed, Benjamin & Baveja, Alok, 2023. "Managing resource allocation for the recruitment stocking problem," Omega, Elsevier, vol. 120(C).
    7. Jahani, Hamed & Chaleshtori, Amir Eshaghi & Khaksar, Seyed Mohammad Sadegh & Aghaie, Abdollah & Sheu, Jiuh-Biing, 2022. "COVID-19 vaccine distribution planning using a congested queuing system—A real case from Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).

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