Many industries are looking for ways to economically use truck/rail/ship fitted with drone technologies to augment the “last mile” delivery effort. While drone technologies abound, few, if any studies look at the proper configuration of the drone based on significant features of the problem: delivery density, operating area, drone range, and speed. Here, we first present the truck-drone problem and then invert the network routing problem such that the best case drone speed and range are fitted to the truck for a given scenario based on the network delivery density. By inverting the problem, a business can quickly determine the drone configuration (proper drone range and speed) necessary to optimize the delivery system. Additionally, we provide a more usable version of the truck-drone routing problem as a mixed integer program that can be easily adopted with standardized software used to solve linear programming. Furthermore, our computational metaheuristics and experiments conducted in support of this work are available for download. The metaheuristics used herein surpass current best-in-class algorithms found in literature.

中文翻译：

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逆转卡车无人机网络问题以找到最佳案例配置

许多行业正在寻找经济地使用配备了无人机技术的卡车/铁路/轮船的方法，以增加“最后一英里”的交付工作量。尽管无人机技术比比皆是，但很少有研究基于问题的重要特征（交付密度，工作区域，无人机范围和速度）来研究无人机的正确配置。在这里，我们首先提出卡车无人驾驶问题，然后反转网络路由问题，以便根据网络交付密度为给定场景为卡车配备最佳情况的无人机速度和航程。通过解决问题，企业可以快速确定优化交付系统所需的无人机配置（适当的无人机范围和速度）。另外，我们提供了卡车无人驾驶问题更有用的版本，作为混合整数程序，可以很容易地与用于解决线性规划问题的标准化软件一起采用。此外，我们为支持这项工作而进行的计算元启发式方法和实验也可以下载。本文使用的元启发式算法超过了文献中当前的同类最佳算法。