Delivery network design of a locker-drone delivery system

AbstractDrones are increasingly used for last-mile delivery due to their speed and cost-effectiveness. This study focuses on a novel locker-drone delivery system, where trucks transport parcels from the warehouse to lockers, and drones complete the final delivery. This system is ideal for community and intra-facility logistics. The research optimises the network design by determining the location of lockers, the number of drones at each locker, and the assignment of demands to lockers, minimising operating costs. Both single-parcel and multi-parcel capacity drones are examined. We build an optimisation model for each system, considering drone service capacity as a critical constraint. We design an algorithm combining average sample approximation and a genetic algorithm to address demand uncertainty. The algorithm's efficiency is validated through comparative analysis with Gurobi. Numerical experiments, using real and generated data, optimise the network design. Results show that the multi-capacity drone system requires fewer lockers and drones than the single-capacity system. Although the single-capacity system yields lower drone delivery costs, it incurs higher truck delivery costs. Additionally, a comprehensive cost analysis compares the cost-efficiency of the locker-drone system with a conventional drone delivery system, revealing the cost-saving advantage of the locker-drone system.Keywords: Dronelogisticssample average approximationgenetic algorithmlast-mile delivery AcknowledgmentsThe authors would like to thank the attendees of the IFAC MIM 2022 conference for constructive revision comments, as well as the invitation of this paper as a possible publication in IJPR from the organisers of the IFAC MIM 2022 conference.Data availability statementThe data supporting this study's findings are available on request from the authors. The data in the Sao Paulo case that support the findings of this study are openly available in Kaggle at http://doi.org/10.34740/kaggle/dsv/195341. The raw data in the Wuhan case were generated at OpenStreetMap. Derived data supporting the findings of this study are available from the corresponding author on request.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is partially supported by the National Natural Science Foundation of China (grant number 72171233, 71801225) and the Hubei Provincial Natural Science Foundation of China [grant number 2022CFB390]. Yeming Gong is supported by Artificial Intelligence in Management Institute and BIC Center at emlyon.Notes on contributorsBipan ZouBipan Zou is a Professor of the School of Business Administration of Zhongnan University of Economics and Law. He received his PhD degree from Huazhong University of Science and Technology. His main research interests include design and operating policies analysis of intelligent warehousing systems, such as the robotic mobile fulfillment system, the robotic compact storage and retrieval system, the drone delivery system, the automated mobile robot delivery system. He has published articles in leading international journals, include Transportation Science, European Journal of Operational Research, International Journal of Production Research etc.Siqing WuSiqing Wu is currently a third-year graduate student. She received the B.S. degree in Logistic management from Zhongnan University of Economics and Law, Wuhan City, China, in 2021. She is currently working toward the M.S. degree in Enterprise Management with Zhongnan University of Economics and Law, Wuhan, China.Yeming GongYeming (Yale) Gong is Head of AIM Institute, Director of Business Intelligence Center (BIC), and a full professor at EMLyon Business School. He published 90+ articles in journals including Production and Operations Management, Transportation Science, IIE Trans., European Journal of Operational Research, International Journal of Production Economics, IJPR, Transportation Research E, Annals of OR, C&IE, JORS, OMEGA, IJIM, IT&People, Computers in Human Behavior, IJHM, JMS, MD, IMDS, and IEEE TEM.Zhe YuanZhe Yuan is an Assistant Professor-Researcher at EMLV Business School. She holds her PhD at CentraleSupélec, University of Paris-Saclay. Her research interests include operations management, warehouse management, interface research between artificial intelligence and management science, and flexibility in supply chain management. She has published 20+ articles in journals such as International Journal of Production Research, International Journal of Production Economics, International Journal of Operations and Production Management, Journal of the Operational Research Society, IEEE Transactions on Engineering Management, OR Spectrum, Transportation Research Part D, Energy Economics, Computers & Industrial Engineering.Yuqian ShiYuqian Shi is studying Management Science at Zhongnan University of Economics and Law in Wuhan City, China. His research interests include operations research, algorithms, and mathematical optimisation.