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Choice-driven location-allocation model for healthcare facility location problem

Author

Listed:
  • Kyosang Hwang

    (KAIST)

  • Tooba Binte Asif

    (KAIST)

  • Taesik Lee

    (KAIST)

Abstract

Improving access to care in medically under-served areas (MUA) is an important task for public health policy in many countries. A lack of access to care in MUAs is often addressed by establishing new service providers, which improves the accessibility in those regions in a geographic sense. However, the geographic accessibility is only a necessary condition for relieving the regions from being under-served; new service providers must be sufficiently attractive for care consumers to use so that the new establishment effectively translates to actual improvement in accessibility to care in the MUAs. In this paper, we first develop a location-allocation model for healthcare facilities that incorporates a choice model to represent care consumers’ preferences and choice decisions on the care facilities. Then we expand the model such that some of the attribute variables in the consumer utility function can be handled as decision variables in the location-allocation optimization. Thus, the proposed model determines the attributes variables of open facilities as well as their locations. Utility of the proposed model is demonstrated by using the Korea’s MUA support program for perinatal care.

Suggested Citation

  • Kyosang Hwang & Tooba Binte Asif & Taesik Lee, 2022. "Choice-driven location-allocation model for healthcare facility location problem," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 1040-1065, December.
    • Handle: RePEc:spr:flsman:v:34:y:2022:i:4:d:10.1007_s10696-021-09441-8
    DOI: 10.1007/s10696-021-09441-8
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    References listed on IDEAS

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    Cited by:

    1. Yingyi Huang & Xinyu Wang & Hongyan Chen, 2022. "Location Selection for Regional Logistics Center Based on Particle Swarm Optimization," Sustainability, MDPI, vol. 14(24), pages 1-10, December.
    2. Vincent Augusto & Nadia Lahrichi & Ettore Lanzarone & Taesik Lee & Jie Song, 2022. "Analytics and Optimization in Healthcare Management," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 821-823, December.

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