Park Hyung Jin, Mirjalili Reza, Côté Murray J, Lim Gino J
Department of Industrial Engineering, University of Houston, Houston, TX, USA.
Department of Health Policy and Management, School of Public Health, Texas A&M University, College Station, TX, USA.
J Intell Robot Syst. 2022;105(2):38. doi: 10.1007/s10846-022-01632-1. Epub 2022 Jun 6.
A critical component in the public health response to pandemics is the ability to determine the spread of diseases via diagnostic testing kits. Currently, diagnostic testing kits, treatments, and vaccines for the COVID-19 pandemic have been developed and are being distributed to communities worldwide, but the spread of the disease persists. In conjunction, a strong level of social distancing has been established as one of the most basic and reliable ways to mitigate disease spread. If home testing kits are safely and quickly delivered to a patient, this has the potential to significantly reduce human contact and reduce disease spread before, during, and after diagnosis. This paper proposes a diagnostic testing kit delivery scheduling approach using the Mothership and Drone Routing Problem (MDRP) with one truck and multiple drones. Due to the complexity of solving the MDRP, the problem is decomposed into 1) truck scheduling to carry the drones and 2) drone scheduling for actual delivery. The truck schedule (TS) is optimized first to minimize the total travel distance to cover patients. Then, the drone flight schedule is optimized to minimize the total delivery time. These two steps are repeated until it reaches a solution minimizing the total delivery time for all patients. Heuristic algorithms are developed to further improve the computational time of the proposed model. Experiments are made to show the benefits of the proposed approach compared to the commonly performed face-to-face diagnosis via the drive-through testing sites. The proposed solution method significantly reduced the computation time for solving the optimization model (less than 50 minutes) compared to the exact solution method that took more than 10 hours to reach a 20% optimality gap. A modified basic reproduction rate (i.e., ) is used to compare the performance of the drone-based testing kit delivery method to the face-to-face diagnostic method in reducing disease spread. The results show that our proposed method ( = 0.002) outperformed the face-to-face diagnostic method ( = 0.0153) by reducing by 7.5 times.
公共卫生应对大流行的一个关键要素是通过诊断检测试剂盒来确定疾病传播情况的能力。目前，针对新冠疫情的诊断检测试剂盒、治疗方法和疫苗已经研发出来并正在全球各地社区分发，但疾病仍在传播。与此同时，保持高度的社交距离已被确立为减轻疾病传播最基本、最可靠的方法之一。如果能将家用检测试剂盒安全、快速地送达患者手中，就有可能在诊断前、诊断期间和诊断后显著减少人际接触，降低疾病传播。本文提出了一种诊断检测试剂盒配送调度方法，该方法使用母舰和无人机路径规划问题（MDRP），涉及一辆卡车和多架无人机。由于求解MDRP的复杂性，该问题被分解为：1）运送无人机的卡车调度；2）实际配送的无人机调度。首先优化卡车调度（TS），以最小化覆盖患者的总行驶距离。然后，优化无人机飞行调度，以最小化总配送时间。重复这两个步骤，直到找到一个能使所有患者的总配送时间最小化的解决方案。开发了启发式算法以进一步缩短所提模型的计算时间。通过实验展示了所提方法相较于通过免下车检测点进行的常规面对面诊断的优势。与耗时超过10小时才能达到20%最优差距的精确求解方法相比，所提求解方法显著缩短了求解优化模型的计算时间（不到50分钟）。使用修正的基本再生数（即 ）来比较基于无人机的检测试剂盒配送方法与面对面诊断方法在减少疾病传播方面的性能。结果表明，我们所提方法（ = 0.002）在降低 方面比面对面诊断方法（ = 0.0153）优7.5倍。
