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Analysis of maximal covering location-allocation model for congested healthcare systems in user choice environment

Author

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  • Mahsa Pouraliakbari
  • Mohammad Mohammadi
  • Abolfazl Mirzazadeh

Abstract

This paper attempts to shed light onto the formulation and the solution of a probabilistic maximal covering location model for determining the optimal location of facilities in congested healthcare systems with referral hierarchical structure. We will address healthcare systems operating in a competitive setting and when the customers choose the facilities they patronise, by the travel time to the hospitals and service costs at each hospital. The hospitals have several low-level sections for offering low level services (such as primary services) and several high-level sections for offering high level services (such as professional services) and the patients will refer to different sections of hospitals according to their requirements and their physical conditions. Our objective is to minimise the total amount of demand that is lost in the system. To solve the model, two meta-heuristic algorithms, including population-based simulated annealing (PBSA) and ant colony optimisation (ACO) have been executed and analysed using statistical test and TOPSIS method to determine which algorithm works better.

Suggested Citation

  • Mahsa Pouraliakbari & Mohammad Mohammadi & Abolfazl Mirzazadeh, 2018. "Analysis of maximal covering location-allocation model for congested healthcare systems in user choice environment," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 28(2), pages 240-274.
    • Handle: RePEc:ids:ijisen:v:28:y:2018:i:2:p:240-274
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    Cited by:

    1. Pouraliakbari-Mamaghani, Mahsa & Saif, Ahmed & Kamal, Noreen, 2023. "Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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