To effectively mitigate the spread of infections during public health crises, precise and timely distribution of medical supplies is crucial. This paper proposes the integration of the metro-based underground logistics system (M-ULS) into the delivery process to address the vehicle routing problem (VRP). Considering various factors related to the metro-based VRP in public health emergencies, we formulate a mixed-integer nonlinear function model that aims to minimize total delivery costs while maximizing the load factor of medical vans and the average demand index concerning demand urgency and satisfaction rate of medical centers. To achieve this, we develop an improved adaptive genetic algorithm (IAGA) that incorporates adaptive crossover, adaptive mutation, and an elitist strategy. A case study is conducted to verify and analyze the performance of the optimized model and solving algorithm. Extensive numerical experiments are carried out to assess the economical and efficient advantages of M-ULS and the computational efficiency and solution quality of IAGA. Sensitivity analysis is conducted to evaluate the impact of medical van capacity on the routes. Lastly, we consider inter-medical-center transfer (IMCT) to further enhance emergency health response.
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