Abstract
Unmanned aerial vehicles (UAVs), also known as drones, have emerged as a promising mode of fast, energy-efficient, and cost-effective package delivery. A considerable number of works have studied different aspects of drone package delivery service by a supplier, one of which is delivery planning. However, existing works addressing the planning issues consider a simple case of perfect delivery without service interruption, e.g., due to accident which is common and realistic. Therefore, this paper introduces the joint ground and aerial delivery service optimization and planning (GADOP) framework. The framework explicitly incorporates uncertainty of drone package delivery, i.e., takeoff and breakdown conditions. The GADOP framework aims to minimize the total delivery cost given practical constraints, e.g., traveling distance limit. Specifically, we formulate the GADOP framework as a three-stage stochastic integer programming model. To deal with the high complexity issue of the problem, a decomposition method is adopted. Then, the performance of the GADOP framework is evaluated by using two data sets including Solomon benchmark suite and the real data from one of the Singapore logistics companies. The performance evaluation clearly shows that the GADOP framework can achieve significantly lower total payment than that of the baseline methods which do not take uncertainty into account.
Abstract (translated)
无人驾驶飞行器（UAV），也称为无人机，已经成为一种快速，节能且经济有效的包裹递送模式。相当多的作品研究了供应商的无人机包裹递送服务的不同方面，其中之一是交付计划。然而，解决规划问题的现有工作考虑了完美交付的简单情况，而没有服务中断，例如，由于常见且现实的事故。因此，本文介绍了联合地面和空中交付服务的优化和规划（GADOP）框架。该框架明确地包含了无人机包裹递送的不确定性，即起飞和击穿条件。 GADOP框架旨在在给定实际约束（例如行驶距离限制）的情况下最小化总交付成本。具体来说，我们将GADOP框架表示为三阶段随机整数规划模型。为了解决问题的高复杂性问题，采用分解方法。然后，使用包括Solomon基准套件在内的两个数据集以及来自新加坡某物流公司的真实数据来评估GADOP框架的性能。绩效评估清楚地表明，GADOP框架可以实现比不考虑不确定性的基线方法显着降低的总支付额。
URL
https://arxiv.org/abs/1808.04617
