IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5998-d640132.html
   My bibliography  Save this article

Energy Consumption Model of Aerial Urban Logistic Infrastructures

Author

Listed:
  • Giuseppe Aiello

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

  • Rosalinda Inguanta

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

  • Giusj D’Angelo

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

  • Mario Venticinque

    (National Council of Research, Istituto per i Sistemi Agricoli e Forestali del Mediterraneo (ISAFoM), 80055 Catania, Italy)

Abstract

In the last decade, logistic systems based on small aerial vehicles (drones) have become attractive for urban delivery operations as a sustainable alternative to ground vehicles because they are not affected by the congestion of the road network, thus allowing for faster and more reliable services. Aerial logistic systems, however, require a substantially different approach to operations management and need specifically designed supportive infrastructures. While the research on urban aerial delivery mostly focuses on the optimization vehicle operations, the appropriate design of supportive infrastructures is seldom considered. This paper focuses on the energy efficiency of aerial logistic systems, and proposes a new original methodology to obtain a realistic estimate of the overall energy consumed by a swarm of drones employed for urban delivery, taking into account the extension of the area served and its specific features. The methodology proposed offers relevant information for the decision problems related to the appropriate sizing of the infrastructures, the dimensioning of the swarm of drones and the capacity of the energy storage system. The paper also reports a comparison with ground vehicles in the same scenarios, showing the relevant tradeoffs. The results obtained demonstrate how an appropriate design of the supportive infrastructures for urban aerial logistics may significantly impact the overall efficiency of the delivery system.

Suggested Citation

  • Giuseppe Aiello & Rosalinda Inguanta & Giusj D’Angelo & Mario Venticinque, 2021. "Energy Consumption Model of Aerial Urban Logistic Infrastructures," Energies, MDPI, vol. 14(18), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5998-:d:640132
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5998/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/18/5998/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mauro Dell’Amico & Roberto Montemanni & Stefano Novellani, 2020. "Matheuristic algorithms for the parallel drone scheduling traveling salesman problem," Annals of Operations Research, Springer, vol. 289(2), pages 211-226, June.
    2. Sivaramakrishnan Srinivasan & Chandra Bhat, 2005. "Modeling household interactions in daily in-home and out-of-home maintenance activity participation," Transportation, Springer, vol. 32(5), pages 523-544, September.
    3. Lachapelle, Ugo & Burke, Matthew & Brotherton, Aiden & Leung, Abraham, 2018. "Parcel locker systems in a car dominant city: Location, characterisation and potential impacts on city planning and consumer travel access," Journal of Transport Geography, Elsevier, vol. 71(C), pages 1-14.
    4. Comi, Antonio, 2020. "A modelling framework to forecast urban goods flows," Research in Transportation Economics, Elsevier, vol. 80(C).
    5. Julian Allen & Michael Browne & Allan Woodburn & Jacques Leonardi, 2012. "The Role of Urban Consolidation Centres in Sustainable Freight Transport," Transport Reviews, Taylor & Francis Journals, vol. 32(4), pages 473-490, April.
    6. Wang, Yuan & Zhang, Dongxiang & Liu, Qing & Shen, Fumin & Lee, Loo Hay, 2016. "Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 279-293.
    7. 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.
    8. Wang, Zheng & Sheu, Jiuh-Biing, 2019. "Vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 350-364.
    9. 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.
    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. József Vásárhelyi & Omar M. Salih & Hussam Mahmod Rostum & Rabab Benotsname, 2023. "An Overview of Energies Problems in Robotic Systems," Energies, MDPI, vol. 16(24), pages 1-24, December.
    2. Ranjit R. Desai & Eric Hittinger & Eric Williams, 2022. "Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market," Energies, MDPI, vol. 15(13), pages 1-25, June.

    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. 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.
    3. Li, Hongqi & Chen, Jun & Wang, Feilong & Bai, Ming, 2021. "Ground-vehicle and unmanned-aerial-vehicle routing problems from two-echelon scheme perspective: A review," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1078-1095.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Li, Hongqi & Wang, Haotian & Chen, Jun & Bai, Ming, 2020. "Two-echelon vehicle routing problem with time windows and mobile satellites," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 179-201.
    9. Scherr, Yannick Oskar & Hewitt, Mike & Neumann Saavedra, Bruno Albert & Mattfeld, Dirk Christian, 2020. "Dynamic discretization discovery for the service network design problem with mixed autonomous fleets," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 164-195.
    10. Yin, Yunqiang & Yang, Yongjian & Yu, Yugang & Wang, Dujuan & Cheng, T.C.E., 2023. "Robust vehicle routing with drones under uncertain demands and truck travel times in humanitarian logistics," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    11. Xia, Yang & Zeng, Wenjia & Zhang, Canrong & Yang, Hai, 2023. "A branch-and-price-and-cut algorithm for the vehicle routing problem with load-dependent drones," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 80-110.
    12. Roberto Roberti & Mario Ruthmair, 2021. "Exact Methods for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 55(2), pages 315-335, March.
    13. 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).
    14. 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.
    15. Meng, Shanshan & Guo, Xiuping & Li, Dong & Liu, Guoquan, 2023. "The multi-visit drone routing problem for pickup and delivery services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    16. Tamke, Felix & Buscher, Udo, 2021. "A branch-and-cut algorithm for the vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 174-203.
    17. Archetti, Claudia & Peirano, Lorenzo & Speranza, M. Grazia, 2022. "Optimization in multimodal freight transportation problems: A Survey," European Journal of Operational Research, Elsevier, vol. 299(1), pages 1-20.
    18. John Olsson & Daniel Hellström & Henrik Pålsson, 2019. "Framework of Last Mile Logistics Research: A Systematic Review of the Literature," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    19. Kitjacharoenchai, Patchara & Min, Byung-Cheol & Lee, Seokcheon, 2020. "Two echelon vehicle routing problem with drones in last mile delivery," International Journal of Production Economics, Elsevier, vol. 225(C).
    20. 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).

    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:jeners:v:14:y:2021:i:18:p:5998-:d:640132. 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.