IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i20p3744-d939649.html
   My bibliography  Save this article

Vehicle and UAV Collaborative Delivery Path Optimization Model

Author

Listed:
  • Jianxun Li

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710048, China)

  • Hao Liu

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710048, China)

  • Kin Keung Lai

    (International Business School, Shaanxi Normal University, Xi’an 710048, China)

  • Bhagwat Ram

    (Centre for Digital Transformation, Indian Institute of Management Ahmedabad, Vastrapur 380015, India)

Abstract

In the context of frequent public emergencies, emergency logistics distribution is particularly critical, and because of the unique advantages of unmanned aerial vehicles (UAVs), the model of coordinated delivery of vehicles and UAVs is gradually becoming an essential form of emergency logistics distribution. However, the omission of start-up costs prevents the cost of UAV battery replacement and the sorting, assembly and verification of packages from being factored into the total cost. Furthermore, most existing models focus on route optimization and delivery cost, which cannot fully reflect the customer’s desire for service satisfaction under emergency conditions. It is necessary to convert the unsatisfactory degree of time window into a penalty cost rather than a model constraint. Additionally, there is a lack of analysis on the mutual waiting cost between vehicles and UAVs when one of them is performing delivery tasks. Considering the effects of the time window, customer demand, maximum load capacity, and duration of distribution benefits, we propose a collaborative delivery path optimization model for vehicles and UAVs to minimize the total distribution cost. A genetic algorithm is used to obtain the model solution under the constraints of distribution subloops, distribution order, and take-off and landing nodes. To assess the efficacy of the vehicle and UAV collaborative delivery path optimization model, this paper employs a county-level district in Xi’an city as a pilot area for an emergency delivery. Compared with the vehicle-alone delivery model, the UAV-alone delivery model and vehicle-UAV collaborative delivery model, this model can significantly reduce the utilization of distribution vehicles while also significantly lowering the start-up cost, waiting cost and penalty cost. Thus, the model can effectively improve delivery timeliness and customer satisfaction. The total cost of this model is 39.2% less than that of the vehicle-alone delivery model and 16.5% less than that of the UAV-alone delivery model. Although its delivery cost is slightly higher than the vehicle-UAV collaborative delivery model, the reduction in the start-up cost and penalty cost decrease the overall cost of distribution by 11.8%. This suggests that to cut costs of all sizes and conserve half of the resources used by vehicles, employing the vehicle-UAV collaborative delivery model for emergency distribution is preferable. Moreover, the model integrating the start-up cost, penalty cost, waiting cost, etc., can more effectively express the requirements of timeliness for UAV delivery under emergency conditions.

Suggested Citation

  • Jianxun Li & Hao Liu & Kin Keung Lai & Bhagwat Ram, 2022. "Vehicle and UAV Collaborative Delivery Path Optimization Model," Mathematics, MDPI, vol. 10(20), pages 1-22, October.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3744-:d:939649
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/20/3744/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/20/3744/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuyu Li & Wei Yang & Bo Huang, 2020. "Impact of UAV Delivery on Sustainability and Costs under Traffic Restrictions," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, August.
    2. Joonyup Eun & Byung Duk Song & Sangbok Lee & Dae-Eun Lim, 2019. "Mathematical Investigation on the Sustainability of UAV Logistics," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    3. Ju Wang & Guoqiang Wang & Xiaoxuan Hu & He Luo & Haiqing Xu, 2020. "Cooperative Transmission Tower Inspection with a Vehicle and a UAV in Urban Areas," Energies, MDPI, vol. 13(2), pages 1-17, January.
    4. Lulua Bahrainwala & Astrid M Knoblauch & Andry Andriamiadanarivo & Mohamed Mustafa Diab & Jesse McKinney & Peter M Small & James G Kahn & Elizabeth Fair & Niaina Rakotosamimanana & Simon Grandjean Lap, 2020. "Drones and digital adherence monitoring for community-based tuberculosis control in remote Madagascar: A cost-effectiveness analysis," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-19, July.
    5. 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).
    6. 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.
    7. Myeong-hwan Hwang & Hyun-Rok Cha & Sung Yong Jung, 2018. "Practical Endurance Estimation for Minimizing Energy Consumption of Multirotor Unmanned Aerial Vehicles," Energies, MDPI, vol. 11(9), pages 1-11, August.
    8. Salama, Mohamed R. & Srinivas, Sharan, 2022. "Collaborative truck multi-drone routing and scheduling problem: Package delivery with flexible launch and recovery sites," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    9. John Gunnar Carlsson & Siyuan Song, 2018. "Coordinated Logistics with a Truck and a Drone," Management Science, INFORMS, vol. 64(9), pages 4052-4069, September.
    10. 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.
    11. Gohram Baloch & Fatma Gzara, 2020. "Strategic Network Design for Parcel Delivery with Drones Under Competition," Transportation Science, INFORMS, vol. 54(1), pages 204-228, January.
    12. Kangzhou Wang & Biao Yuan & Mengting Zhao & Yuwei Lu, 2020. "Cooperative route planning for the drone and truck in delivery services: A bi-objective optimisation approach," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(10), pages 1657-1674, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuan Gao & Qian Zhang & Chun Kit Lau & Bhagwat Ram, 2022. "Robust Appointment Scheduling in Healthcare," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    2. Faten Aljalaud & Heba Kurdi & Kamal Youcef-Toumi, 2023. "Bio-Inspired Multi-UAV Path Planning Heuristics: A Review," Mathematics, MDPI, vol. 11(10), pages 1-35, May.

    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. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    2. Madani, Batool & Ndiaye, Malick & Salhi, Said, 2024. "Hybrid truck-drone delivery system with multi-visits and multi-launch and retrieval locations: Mathematical model and adaptive variable neighborhood search with neighborhood categorization," European Journal of Operational Research, Elsevier, vol. 316(1), pages 100-125.
    3. Zhao, Lei & Bi, Xinhua & Li, Gendao & Dong, Zhaohui & Xiao, Ni & Zhao, Anni, 2022. "Robust traveling salesman problem with multiple drones: Parcel delivery under uncertain navigation environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    4. Jiang, Jie & Dai, Ying & Yang, Fei & Ma, Zujun, 2024. "A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services," European Journal of Operational Research, Elsevier, vol. 312(1), pages 125-137.
    5. Schmidt, Sebastian & Saraceni, Adriana, 2024. "Consumer acceptance of drone-based technology for last mile delivery," Research in Transportation Economics, Elsevier, vol. 103(C).
    6. Salama, Mohamed R. & Srinivas, Sharan, 2022. "Collaborative truck multi-drone routing and scheduling problem: Package delivery with flexible launch and recovery sites," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    7. Yu, Shaohua & Puchinger, Jakob & Sun, Shudong, 2022. "Van-based robot hybrid pickup and delivery routing problem," European Journal of Operational Research, Elsevier, vol. 298(3), pages 894-914.
    8. Chen, Cheng & Demir, Emrah & Huang, Yuan, 2021. "An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1164-1180.
    9. Yang, Yu & Yan, Chiwei & Cao, Yufeng & Roberti, Roberto, 2023. "Planning robust drone-truck delivery routes under road traffic uncertainty," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1145-1160.
    10. 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.
    11. 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.
    12. Tengkuo Zhu & Stephen D. Boyles & Avinash Unnikrishnan, 2024. "Battery Electric Vehicle Traveling Salesman Problem with Drone," Networks and Spatial Economics, Springer, vol. 24(1), pages 49-97, March.
    13. 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.
    14. Mohammad Moshref-Javadi & Kristof P. Cauwenberghe & Brent A. McCunney & Ahmad Hemmati, 2023. "Enabling same-day delivery using a drone resupply model with transshipment points," Computational Management Science, Springer, vol. 20(1), pages 1-31, December.
    15. Yin, Yunqiang & Li, Dongwei & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Wang, Sutong, 2023. "A branch-and-price-and-cut algorithm for the truck-based drone delivery routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1125-1144.
    16. Sun, Xuting & Kuo, Yong-Hong & Xue, Weili & Li, Yanzhi, 2024. "Technology-driven logistics and supply chain management for societal impacts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    17. Tiniç, Gizem Ozbaygin & Karasan, Oya E. & Kara, Bahar Y. & Campbell, James F. & Ozel, Aysu, 2023. "Exact solution approaches for the minimum total cost traveling salesman problem with multiple drones," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 81-123.
    18. Amine Masmoudi, M. & Mancini, Simona & Baldacci, Roberto & Kuo, Yong-Hong, 2022. "Vehicle routing problems with drones equipped with multi-package payload compartments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    19. 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).
    20. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.

    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:gam:jmathe:v:10:y:2022:i:20:p:3744-:d:939649. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.