Article Dans Une Revue Computers and Operations Research Année : 2026

A hybrid memetic metaheuristic for medical staff assignment in major public health emergencies

Résumé

During major public health emergencies, effective assignment of medical staff is crucial for saving lives and controlling the spread of epidemics. This work focuses on the assignment of doctors and nurses to hospitals to form treatment groups that carry out patient treatment tasks. We consider the practical constraints of skill types of medical staff and the severity of patients’ conditions and propose a mixed integer programming model with the objective of maximizing demand satisfaction and personnel skill matching. To solve this problem, we introduce a hybrid memetic search algorithm that combines a specialized crossover operator for generating promising offspring solutions and a variable neighborhood search procedure to improve their quality. Computational results demonstrate that our algorithm outperforms the general mixed integer programming solver GUROBI. The key components of the proposed algorithm are experimentally analyzed and managerial insights are derived.

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Dates et versions

hal-05243676 , version 1 (07-09-2025)

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Citer

Yang Wang, He Zheng, Zequn Wei, Christophe Wilbaut, Saïd Hanafi. A hybrid memetic metaheuristic for medical staff assignment in major public health emergencies. Computers and Operations Research, 2026, 185, pp.107256. ⟨10.1016/j.cor.2025.107256⟩. ⟨hal-05243676⟩
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