Parcel Consolidation Approach and Routing Algorithm for Last-Mile Delivery by Unmanned Aerial Vehicles

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Parcel Consolidation Approach and Routing Algorithm for Last-Mile Delivery by Unmanned Aerial Vehicles 28 Pages Posted: 6 Sep 2022 See all articles by Xiaohui LiXiaohui Liaffiliation not provided to SSRNPengyu YanUniversity of Electronic Science and Technology of ChinaKaize YuUniversity of Electronic Science and Technology of ChinaPeifan Liaffiliation not provided to SSRN Abstract The last-mile delivery by unmanned aerial vehicles (UAVs), emerging from the online grocery retailing industry, has attached much attention and interest from the scientific and industrial communities. In reality, the online retailing platform receives grocery orders randomly sent by customers and promises ultrafast delivery service to the customers via. two-echelon logistics and distribution network (2E-LDN). The uncertain arrivals of the orders have a nontrivial negative effect on the performance of the whole delivery network, which has not been well studied in the literature. This paper focuses on the operational decisions at a transshipment site in a 2-ELDN, regarding the parcel consolidation and the UAVs routes for the last-mile delivery from the transshipment site to customers. First, this study proposes a nonmyopia consolidation policy considering the upcoming grocery parcels with random arrival times to minimize the total delivery cost including the possible penalty cost due to the delivery delay to the customer. To make the problem tractable in the real-time computational setting, an approximation method based on Bayesian estimation is proposed to reduce a large number of random arrival scenarios of upcoming parcels to an expected scenario. The problem is then approached to a deterministic model under the expected scenario. Subsequently, a discrete particle swarm optimization (DPSO) algorithm is developed to solve the model. In the algorithm, a neighborhood search based on reinforcement learning is developed to improve the quality of the particles in the searching process. In addition, a novel decoding method is designed to evaluate the particles with respect to the constraints of the load and battery capacities. The experimental results based on a case study and variations of benchmarks validate the performance of the proposed consolidation policy and the UAV routing algorithm. Keywords: Last-mile delivery, consolidation, heuristics, neighborhood search Suggested Citation: Suggested Citation Li, Xiaohui and Yan, Pengyu and Yu, Kaize and Li, Peifan, Parcel Consolidation Approach and Routing Algorithm for Last-Mile Delivery by Unmanned Aerial Vehicles. Available at SSRN: https://ssrn.com/abstract=4211353 Xiaohui Li affiliation not provided to SSRN ( email ) No Address Available Pengyu Yan (Contact Author) University of Electronic Science and Technology of China ( email ) Kaize Yu University of Electronic Science and Technology of China ( email ) China Peifan Li affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 2 Abstract Views 3 PlumX Metrics Related eJournals Information Management eJournal Follow Information Management eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 61 PAPERS 1,538 Information Theory & Research eJournal Follow Information Theory & Research eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 59 PAPERS 498 Libraries & Information Technology eJournal Follow Libraries & Information Technology eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 44 PAPERS 3,418 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.234 seconds