PDF(Pubmed)
Abstract：
In the COVID-19 pandemic, it is essential to transport medical supplies to specific locations accurately, safely, and promptly on time. The application of drones for medical supplies delivery can break ground traffic restrictions, shorten delivery time, and achieve the goal of contactless delivery to reduce the likelihood of contacting COVID-19 patients. However, the existing optimization model for drone delivery is cannot meet the requirements of medical supplies delivery in public health emergencies. Therefore, this paper proposes a bi-objective mixed integer programming model for the multi-trip drone location routing problem, which allows simultaneous pick-up and delivery, and shorten the time to deliver medical supplies in the right place. Then, a modified NSGA-II (Non-dominated Sorting Genetic Algorithm II) which includes double-layer coding, is designed to solve the model. This paper also conducts multiple sets of data experiments to verify the performance of modified NSGA-II. Comparing with separate pickup and delivery modes, this study demonstrates that the proposed optimization model with simultaneous pickup and delivery mode achieves a shorter time, is safer, and saves more resources. Finally, the sensitivity analysis is conducted by changing some parameters, and providing some reference suggestions for medical supplies delivery management via drones.
摘要：
在COVID-19大流行中，必须将医疗用品准确运输到特定位置，安全,并及时。应用无人机运送医疗物资可以打破地面交通限制,缩短交货时间，并实现非接触式给药的目标，以降低接触COVID-19患者的可能性。然而,现有的无人机配送优化模型无法满足突发公共卫生事件中医疗物资配送的要求。因此,本文针对多行程无人机定位路径问题,提出了一种双目标混合整数规划模型,它允许同时提货和送货，并缩短在正确的地方运送医疗用品的时间。然后，一种改进的NSGA-II(非支配排序遗传算法II)，包括双层编码，是为了求解模型而设计的。本文还进行了多组数据实验，以验证改良的NSGA-II的性能。与单独的取货和送货模式相比，这项研究表明，提出的同时取货和交货模式的优化模型实现了更短的时间，更安全,节省更多资源。最后,敏感性分析是通过改变一些参数来进行的，为无人机医疗物资配送管理提供参考建议。
