Contents lists available at ScienceDirect Transportation Research Part C journal homepage: www.elsevier.com/locate/trc A range-restricted recharging station coverage model for drone delivery service planning Insu Hong a, ⁎ , Michael Kuby b , Alan T. Murray c a Department of Geology and Geography, West Virginia University, United States b School of Geographical Sciences and Urban Planning, Arizona State University, United States c Department of Geography, University of California at Santa Barbara, United States ARTICLE INFO Keywords: Unmanned aerial vehicles Spatial optimization Euclidean Shortest Path GIS Location modeling ABSTRACT Unmanned Aerial Vehicles (UAVs) are attracting significant interest for delivery service of small packages in urban areas. The limited flight range of electric drones powered by batteries or fuel cells requires refueling or recharging stations for extending coverage to a wider area. To develop such service, optimization methods are needed for designing a network of station locations and delivery routes. Unlike ground-transportation modes, however, UAVs do not follow a fixed network but rather can fly directly through continuous space. But, paths must avoid barriers and other obstacles. In this paper, we propose a new location model to support spatially configuring a system of recharging stations for commercial drone delivery service, drawing on literature from planar-space routing, range-restricted flow-refueling location, and maximal coverage location. We present a mixed-integer programming formulation and an efficient heuristic algorithm, along with results for a large case study of Phoenix, AZ to demonstrate the effectiveness and efficiency of the model. 1. Introduction Unmanned aerial vehicles (UAVs), or drones, have developed rapidly for commercial and personal uses, from military to sur- veillance/monitoring, journalism, scientific research, photography, emergency response, and recreational activities (Finn and Wright, 2012; Clarke, 2014; Sandbrook, 2015). Deploying drones for delivery service of small packages has attracted much attention, and several companies and public agencies have proposed or tested drone delivery service at a small scale (Hern, 2014; Murray and Chu, 2015; Ha et al., 2015a,b; Weise, 2017). Amazon (2017) is considering a premium delivery service called Amazon Prime Air, which would provide rapid delivery of packages within 30 min of ordering online. While not ready to completely replace the familiar delivery trucks, anytime soon, drones appear well-suited to augment existing road deliveries for high-margin, last-minute service to single-family homes and stand-alone businesses, and to bypass road congestion (Agatz et al., 2015). Another promising application is for reaching areas lacking road access, such as small islands and rainforest (Zhang and Kovacs, 2012; Hern, 2014; Toor, 2016). To develop a stand-alone drone delivery service, a route planning strategy is necessary, based on efficient delivery routes in continuous two-dimensional space. Although a drone may not need to follow a pre-defined transportation network, barriers such as high-rise buildings, mountains and flight-restricted zones may impede a more direct flight path. In the literature, the problem of finding the best obstacle-avoiding route is known as the Euclidean Shortest Path (ESP) problem (Lozano-Pérez and Wesley, 1979; Asano et al., 1986; Hong and Murray, 2013). https://doi.org/10.1016/j.trc.2018.02.017 Received 15 November 2016; Received in revised form 9 October 2017; Accepted 20 February 2018 ⁎ Corresponding author. E-mail address: insu.hong@mail.wvu.edu (I. Hong). Transportation Research Part C 90 (2018) 198–212 0968-090X/ © 2018 Elsevier Ltd. All rights reserved. T