Battery Electric Vehicle Traveling Salesman Problem with Drone

Tengkuo Zhu, Stephen D. Boyles, Avinash Unnikrishnan
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Abstract

The idea of deploying electric vehicles and unmanned aerial vehicles (UAVs), also known as drones, to deliver packages in logistics operations has attracted increasing attention in the past few years. In this paper, we propose an innovative problem where a battery electric vehicle (BEV) paired with drone is utilized to deliver first-aid items in a rural area. This problem is termed battery electric vehicle traveling salesman problem with drone (BEVTSPD). In BEVTSPD, the BEV and the drone perform delivery tasks coordinately while the BEV can serve as a drone hub. The BEV can also refresh its battery energy to full capacity in battery-swap stations available in the network. An arc-based mixed-integer programming model defined in a multigraph is presented for BEVTSPD. An exact branch-and-price (BP) algorithm and a Variable Neighborhood Search (VNS) heuristic are developed to solve instances with up to 25 customers in one minute. Numerical experiments show that the heuristic is much more efficient than solving the arc-based model using the ILOG CPLEX solver and BP algorithm. A real-world case study and the sensitivity analysis of different parameters are also conducted and presented. The results indicate that drone speed has a more significant effect on delivery time than the BEV’s driving range.

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用无人机解决电池电动汽车旅行推销员问题
在过去几年中,在物流业务中部署电动汽车和无人驾驶飞行器(UAV)(也称为无人机)来运送包裹的想法引起了越来越多的关注。在本文中,我们提出了一个创新问题,即利用电池电动车(BEV)搭配无人机在农村地区运送急救物品。这个问题被称为带无人机的电池电动车旅行推销员问题(BEVTSPD)。在 BEVTSPD 中,BEV 和无人机协调执行送货任务,而 BEV 可以充当无人机枢纽。BEV 还可以在网络中的电池交换站将其电池能量充满。本文针对 BEVTSPD 提出了一个在多图中定义的基于弧的混合整数编程模型。开发了一种精确的分支定价(BP)算法和一种可变邻域搜索(VNS)启发式,可在一分钟内解决多达 25 个客户的实例。数值实验表明,启发式比使用 ILOG CPLEX 求解器和 BP 算法求解基于弧的模型要有效得多。此外,还进行了实际案例研究和不同参数的敏感性分析。结果表明,无人机速度对交货时间的影响比 BEV 的行驶里程更为显著。
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