Strategic Network Design for Parcel Delivery with Drones under Competition Gohram Baloch, Fatma Gzara Department of Management Sciences, University of Waterloo, ON Canada N2L 3G1 [email protected] [email protected] This paper studies the economic desirability of UAV parcel delivery and its effect on e-retailer distribution network while taking into account technological limitations, government regulations, and customer behavior. We consider an e-retailer offering multiple same day delivery services including a fast UAV service and develop a distribution network design formulation under service based competition where the services offered by the e-retailer not only compete with the stores (convenience, grocery, etc.), but also with each other. Competition is incorporated using the Multinomial Logit market share model. To solve the resulting nonlinear mathematical formulation, we develop a novel logic-based Benders decomposition approach. We build a case based on NYC, carry out extensive numerical testing, and perform sensitivity analyses over delivery charge, delivery time, government regulations, technological limitations, customer behavior, and market size. The results show that government regulations, technological limitations, and service charge decisions play a vital role in the future of UAV delivery. Key words : UAV; drone; market share models; facility location; logic-based benders decomposition 1. Introduction Unmanned aerial vehicles (UAVs) or drones have been used in military applications as early as 1916 (Cook 2007). As the technology improved, their applications extended to surveillance and moni- toring (Maza et al. 2010, Krishnamoorthy et al. 2012), weather research (Darack 2012), delivery of medical supplies (Wang 2016, Thiels et al. 2015), and emergency response (Adams and Friedland 2011). Yet, when in 2013 Amazon revealed its plan for \Prime Air" service to deliver packages using UAVs within 30 minutes, it was faced with significant skepticism. The idea that our skies 1 Baloch and Gzara: UAV service with competition 2 would be crowded with UAVs sounded like science fiction. While being confident that UAVs will be as common as delivery trucks in a few years, Amazon's CEO Jeff Bezos admitted in a 2014 interview with The Telegraph (Quinn 2015) that regulations lag behind and pose a serious obstacle. Logistics practitioners also stated technology limitations, safety, privacy, and public perception as major issues that may hinder the use of UAV technology for parcel delivery (Lewis 2014a, Keeney 2016, Wang 2016). Despite these hurdles, Amazon's announcement started a race among compa- nies like Google, Walmart, DHL, and Zookal to develop the technology and the logistics strategies to enable the use of UAVs not only in last mile parcel delivery but also in first mile delivery, inter- and intra-facility distribution, and delivery to remote and difficult to access regions (Butter 2015, Hovrtek 2018). A remarkable application is that by DHL's \Parcelcopter 3.0" making 130 successful parcel deliveries in remote areas of Bavaria, Germany in 2017 (Burgess 2017). Recently, Amazon has successfully delivered its first Prime Air package containing a TV streaming stick and a bag of popcorn to a customer in UK (Hern 2016). Other successful applications include hybrid truck-UAV delivery by UPS in Florida, USA (Stewart 2017), and UAV package delivery to islands by Chinese e-commerce giant, Alibaba (Xinhua 2017). Despite these promising applications, UAV parcel delivery is not yet a full scale reality. Whether the attractiveness of the technology will overcome the regulatory and social obstacles is yet to be determined. Unlike trucks, autonomous UAVs fly without a human pilot, are fast as they do not use congested road networks (Lewis 2014b, Wang 2016), and are significantly cheaper (Welch 2015, D'Andrea 2014, Hickey 2014, Keeney 2016). Hence they provide a perfect solution for the e-retail industry. The latter captured 11.7% of the total U.S retail sales in 2016 with a growth rate of 8-12% (Statista 2016, Intelligence 2017). Similar growth is observed globally. For example, in 2016, the Chinese e- retail market captured 15.5% of the total retail sales with a growth rate of 26.2% (ECN 2017). This growth is largely due to millennials who embrace online shopping but are ever more sensitive to delivery time and delivery charge (Hsu 2016). Yet, it is not clear how customer preferences and their sensitivity to delivery charge and delivery time affects their choice of a fast UAV delivery service Baloch and Gzara: UAV service with competition 3 versus traditional in-person shopping. On the other hand, UAVs are limited by package weight, travel range, and landing area. For example Amazon Prime Air can carry a package weighing up to 2.5 kg and travels up to 24 km (Keeney 2016). DHL's Parcelcopter carries a package of up to 2 kg with a travel range of 16 km (Franco 2016). Regulations require that an UAV is monitored by a certified operator even though UAVs like Prime Air and Parcelcopter are autonomous and can operate without human intervention. These limitations together with customer preferences are expected to play a crucial role in determining the future of UAV parcel delivery. In this paper, we study the economic feasibility of UAV parcel delivery in terms of its impact on an e-retailer's distribution network while taking into account customer preferences, locational decisions, and regulatory and technological limitations. These research questions are of most inter- est to an e-retailer like Amazon, that already offers a set of delivery services such as Same day and Prime Now delivery services, and that plans to introduce a new and expedited UAV delivery service: Prime Air. While UAVs may be integrated in a hybrid truck-UAV delivery system, the distinctive feature of instant delivery is compromised and a hybrid system may not yield as fast a delivery as direct drone delivery from the warehouse to the customer location. In order to achieve short delivery times, direct UAV delivery from the e-retailer facilities is required, which may in turn require the redesign of the distribution network partly due to the limited flight range of UAVs. On the other hand, analysis of the top ordered products by Prime Now service reveals that these are mostly consumer products bought for immediate use and are otherwise available at convenience and grocery stores (Chronicle 2015). As such, an expedited UAV delivery service does not only compete with other services offered by the e-retailer but also with physical stores in close proximity to the customer. We investigate the questions that an e-retailer faces when deciding whether or not to offer a UAV parcel delivery service. The decision depends on social, regulatory, and technological challenges facing UAVs. We incorporate social challenges by modelling the market share captured by UAV service as a function of customer preferences for the different online services and in-person shopping, Baloch and Gzara: UAV service with competition 4 as well as their sensitivity to delivery time and delivery cost. We use the Multinomial Logit (MNL) market share model (Cooper, Nakanishi, and Eliashberg 1988) where the market share captured by a service is probabilistic and a function of the utility derived from that service relative to the other services available in the market. We model utility with five attributes: inherent attractiveness of the service, travel time, travel cost, delivery charge, and delivery time. If regulation requires human monitoring of UAVs, their operating cost, and consequently the corresponding delivery charge, would increase. Furthermore, we incorporate technological limitations through allowing different types of packages: those that may be delivered by UAVs and those that may not. Landing area requirements like building type are incorporated in estimating the maximum market share that UAV service may attract. Finally, the flying range is factored into the design of the distribution network to determine whether a customer may be offered a UAV service. Ultimately, we model the following key decisions (1) how many facilities to open and where, (2) which services to offer at an open facility, and (3) which services to be made available to each customer zone. The main contributions of this paper are as follows. To the best of our knowledge, this is the first attempt to pose the above research questions in relation to UAV parcel delivery and its impact on the e-retail industry, and to develop a quantitative model to answer these questions. The model is also generic in nature and several possible extensions are proposed in Section4. We develop a logic-based Benders decomposition (LBBD) approach to solve the nonlinear mixed integer model to optimality and within very short time, a few seconds in most cases. The proposed algorithm is also applicable to existing models in competitive facility location (CFL) literature. Also, our work is the first to use a multionominal logit market share model in CFLP to locate multiple facilities with a profit maximization objective, and present an exact solution approach for such a model. Finally, we construct a new case study based in New York City and perform extensive numerical testing to analyze the economic feasibility and added value of UAV delivery under varying levels of technological limitations, regulatory requirements, and customer preferences. The modelling and analysis presented in this paper may be used not only by e-retailers but by any Baloch and Gzara: UAV service with competition 5 retail business to assess the added value of offering UAV delivery. For example, a business concept under development is to offer a UAV leasing service to local businesses such as pizza restaurants, pharmacies, convenience stores, etc., who would independently operate UAVs to deliver customer orders (Luci 2017). It may also be used by regulating bodies to assess the impact of regulations before putting them in effect. We would like to note that we do not exclude the possibility of using hybrid UAV-truck delivery as that may still be used for existing e-retailer services and would only impact the delivery cost and/or delivery charge of these services, which are parameters in our modelling.