Large-scale emergency medical services scheduling during the outbreak of epidemics

Lubing Wang (), Xufeng Zhao () and Peng Wu ()
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Lubing Wang: Nanjing University of Aeronautics and Astronautics
Xufeng Zhao: Nanjing University of Aeronautics and Astronautics
Peng Wu: Fuzhou University

Annals of Operations Research, 2025, vol. 348, issue 1, No 19, 445-469

Abstract: Abstract This paper studies a new large-scale emergency medical services scheduling (EMSS) problem during the outbreak of epidemics like COVID-19, which aims to determine an optimal scheduling scheme of emergency medical services to minimize the completion time of nucleic acid testing to achieve rapid epidemic interruption. We first analyze the impact of the epidemic spread and assign different priorities to different emergency medical services demand points according to the degree of urgency. Then, we formulate the EMSS as a mixed-integer linear program (MILP) model and analyze its complexity. Given the NP-hardness of the problem, we develop two fast and effective improved discrete artificial bee colony algorithms (IDABC) based on problem properties. Experimental results for a real case and practical-sized instances with up to 100 demand points demonstrate that the IDABC significantly outperforms MILP solver CPLEX and two state-of-the-art metaheuristic algorithms in both solution quality and computational efficiency. In addition, we also propose some managerial implications to support emergency management decision-making.

Keywords: COVID-19; Nucleic acid testing; Emergency management; Medical service scheduling; MILP; Artificial bee colony algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05218-4

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