A hybrid cooperative coevolution approach for robust medical supply chain logistics scheduling during an emerging epidemic

초록

Adequate medical supplies and prompt disposal of medical waste are crucial to effectively controlling an emerging epidemic. However, logistics scheduling within the medical supply chain can be severely impacted during such outbreaks. To optimize the distribution of medical supplies and the collection of resulting medical waste, we propose a hybrid cooperative coevolution approach for robust medical supply chain logistics scheduling during an emerging epidemic. First, we develop a hierarchical medical supply chain model in which various types of medical supplies flow from top to bottom. The susceptible-exposed-infected-vigilant epidemic model is also integrated into this model to predict the demand for medical supplies under conditions of uncertainty. Second, we hybridize a cooperative coevolution algorithm with an ant colony system algorithm to optimize the distribution of medical supplies and the collection of the resulting medical waste, respectively, while considering both operational cost and robustness. Finally, extensive experiments based on Monte Carlo simulations validate the effectiveness and efficiency of the proposed approach. The results indicate that the divide-and-conquer strategy employed by the cooperative coevolution algorithm can mitigate the impact of uncertainty while improving scalability.

키워드