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The Roadside Healthcare Facility Location Problem

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  • de Vries, H.
  • van de Klundert, J.J.
  • Wagelmans, A.P.M.

Abstract

__Abstract__ Providing African truck drivers with adequate access to healthcare is an effective way to reduce the burden and the spread of HIV and other infectious diseases. Therefore, NGO North Star Alliance builds a network of healthcare facilities along major African trucking routes. Choosing the locations of new facilities presents novel and complex optimization problems. This paper considers a general design problem: the Roadside Health Care Facility location Problem (RHFLP). RFHLP entails to select locations for new facilities and to choose for each of these facilities whether or not to add healthcare services for HIV, STIs, Tuberculosis, and/or Malaria to the standard health service package. The objective combines the maximization of the truck driver patient volume at these facilities and the maximization of the extent to which the truck drivers have continuous access to the needed health service packages. We present three measures for continuous access to health services by mobile patients and integrate these measures in a mixed-integer programming formulation for RHFLP. Moreover, we prove the RHFLP to be strongly NP-hard and derive analytical results for the worst-case effects of impreciseness in the input data. We show how large scale real life problem instances can be solved, presenting numerical experiments for the North-South corridor network (Southern and Eastern Africa) and discuss policy implications.

Suggested Citation

  • de Vries, H. & van de Klundert, J.J. & Wagelmans, A.P.M., 2014. "The Roadside Healthcare Facility Location Problem," Econometric Institute Research Papers EI 2014-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:51315
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    1. Núñez Ares, José & de Vries, Harwin & Huisman, Dennis, 2016. "A column generation approach for locating roadside clinics in Africa based on effectiveness and equity," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1002-1016.
    2. Taymaz, S. & Iyigun, C. & Bayindir, Z.P. & Dellaert, N.P., 2020. "A healthcare facility location problem for a multi-disease, multi-service environment under risk aversion," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    3. de Vries, Harwin & Duijzer, Evelot, 2017. "Incorporating driving range variability in network design for refueling facilities," Omega, Elsevier, vol. 69(C), pages 102-114.
    4. de Vries, H. & Westerink-Duijzer, L.E., 2016. "Incorporating Driving Range Variability in Network Design for Refueling Facilities," Econometric Institute Research Papers EI2016-19, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Hamid Mousavi & Soroush Avakh Darestani & Parham Azimi, 2021. "An artificial neural network based mathematical model for a stochastic health care facility location problem," Health Care Management Science, Springer, vol. 24(3), pages 499-514, September.
    6. Harwin de Vries & Joris van de Klundert & Albert Wagelmans, 2021. "Toward Elimination of Infectious Diseases with Mobile Screening Teams: HAT in the DRC," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3408-3428, October.
    7. Núñez Ares, J. & de Vries, H. & Huisman, D., 2015. "A Column Generation Approach for Locating Roadside Clinics in Africa based upon Effectiveness and Equity," Econometric Institute Research Papers EI2015-19, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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    Keywords

    roadside healthcare; truck drivers; facility location; continuous access;
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