IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v214y2019icp220-233.html
   My bibliography  Save this article

Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones

Author

Listed:
  • Jeong, Ho Young
  • Song, Byung Duk
  • Lee, Seokcheon

Abstract

The truck-drone hybrid delivery system uses a truck as a station for drones in addition to its delivery function and is getting attention because the strengths of these individual vehicles can be selectively and synergistically exploited. In this study, we extended the previous vehicle routing models to the hybrid delivery systems by taking into account two important practical issues: the effect of parcel weight on drone energy consumption and restricted flying areas. The flight range of the drones is heavily susceptible to the loaded weight due to the limited battery life. Drones are also not allowed to fly over sensitive facilities regulated by the Federal Aviation Administration (FAA) or temporarily in certain areas due to weather-related conditions. We developed a mathematical model that incorporates these issues and propose a two-phase constructive and search heuristic algorithm to provide computational efficiency of the real-world cases problems. The result of the numerical study demonstrates the effectiveness and efficiency of the proposed algorithm.

Suggested Citation

  • Jeong, Ho Young & Song, Byung Duk & Lee, Seokcheon, 2019. "Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones," International Journal of Production Economics, Elsevier, vol. 214(C), pages 220-233.
    • Handle: RePEc:eee:proeco:v:214:y:2019:i:c:p:220-233
    DOI: 10.1016/j.ijpe.2019.01.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527319300106
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2019.01.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. S Lee, 2013. "Centrality-based ambulance dispatching for demanding emergency situations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(4), pages 611-618, April.
    2. Zhang, Jianghua & Zhao, Yingxue & Xue, Weili & Li, Jin, 2015. "Vehicle routing problem with fuel consumption and carbon emission," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 234-242.
    3. Ting, Chuan-Kang & Liao, Xin-Lan, 2013. "The selective pickup and delivery problem: Formulation and a memetic algorithm," International Journal of Production Economics, Elsevier, vol. 141(1), pages 199-211.
    4. Niels Agatz & Paul Bouman & Marie Schmidt, 2018. "Optimization Approaches for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 52(4), pages 965-981, August.
    5. Chowdhury, Sudipta & Emelogu, Adindu & Marufuzzaman, Mohammad & Nurre, Sarah G. & Bian, Linkan, 2017. "Drones for disaster response and relief operations: A continuous approximation model," International Journal of Production Economics, Elsevier, vol. 188(C), pages 167-184.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2020. "Drone routing with energy function: Formulation and exact algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 364-387.
    2. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.
    3. Zhang, Guowei & Zhu, Ning & Ma, Shoufeng & Xia, Jun, 2021. "Humanitarian relief network assessment using collaborative truck-and-drone system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    4. Wusheng Liu & Wang Li & Qing Zhou & Qian Die & Yan Yang, 2022. "The optimization of the "UAV-vehicle" joint delivery route considering mountainous cities," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-21, March.
    5. Dongwook Kim & Kyungsik Lee & Ilkyeong Moon, 2019. "Stochastic facility location model for drones considering uncertain flight distance," Annals of Operations Research, Springer, vol. 283(1), pages 1283-1302, December.
    6. Wenjuan Hou & Tao Fang & Zhi Pei & Qiao-Chu He, 2020. "Integrated Design of Unmanned Aerial Mobility Network: A Data-Driven Risk-Averse Approach," Papers 2004.13000, arXiv.org.
    7. Moshref-Javadi, Mohammad & Lee, Seokcheon & Winkenbach, Matthias, 2020. "Design and evaluation of a multi-trip delivery model with truck and drones," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    8. Zhang, Jie & Meng, Meng & Wong, Yiik Diew & Ieromonachou, Petros & Wang, David Z.W., 2021. "A data-driven dynamic repositioning model in bicycle-sharing systems," International Journal of Production Economics, Elsevier, vol. 231(C).
    9. Sandun Perera & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2020. "Retail Deliveries by Drones: How Will Logistics Networks Change?," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2019-2034, September.
    10. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development," Post-Print hal-04381308, HAL.
    11. Chiang, Wen-Chyuan & Li, Yuyu & Shang, Jennifer & Urban, Timothy L., 2019. "Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization," Applied Energy, Elsevier, vol. 242(C), pages 1164-1175.
    12. Pei, Zhi & Dai, Xu & Yuan, Yilun & Du, Rui & Liu, Changchun, 2021. "Managing price and fleet size for courier service with shared drones," Omega, Elsevier, vol. 104(C).
    13. Ramaekers Katrien & Caris An & Maes Tabitha & Janssens Gerrit K., 2015. "Pickup and Delivery Selection: Problem Formulation and Extension to Problem Variants," Information Technology and Management Science, Sciendo, vol. 18(1), pages 84-90, December.
    14. Qiqian Zhang & Xiao Huang & Honghai Zhang & Chunyun He, 2023. "Research on Logistics Path Optimization for a Two-Stage Collaborative Delivery System Using Vehicles and UAVs," Sustainability, MDPI, vol. 15(17), pages 1-20, September.
    15. Stefan Poikonen & Bruce Golden, 2020. "The Mothership and Drone Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 249-262, April.
    16. Luigi Di Puglia Pugliese & Francesca Guerriero & Maria Grazia Scutellá, 2021. "The Last-Mile Delivery Process with Trucks and Drones Under Uncertain Energy Consumption," Journal of Optimization Theory and Applications, Springer, vol. 191(1), pages 31-67, October.
    17. Jian Zhou & Meixi Zhang & Sisi Wu, 2022. "Multi-Objective Vehicle Routing Problem for Waste Classification and Collection with Sustainable Concerns: The Case of Shanghai City," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    18. Baals, Julian & Emde, Simon & Turkensteen, Marcel, 2023. "Minimizing earliness-tardiness costs in supplier networks—A just-in-time truck routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 707-741.
    19. Vincent F. Yu & Shih-Wei Lin & Panca Jodiawan & Yu-Chi Lai, 2023. "Solving the Flying Sidekick Traveling Salesman Problem by a Simulated Annealing Heuristic," Mathematics, MDPI, vol. 11(20), pages 1-21, October.
    20. Snežana Tadić & Mladen Krstić & Ljubica Radovanović, 2024. "Assessing Strategies to Overcome Barriers for Drone Usage in Last-Mile Logistics: A Novel Hybrid Fuzzy MCDM Model," Mathematics, MDPI, vol. 12(3), pages 1-25, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:214:y:2019:i:c:p:220-233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.