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Sizing of the Drone Delivery Fleet Considering Energy Autonomy

Author

Listed:
  • Asma Troudi

    (QUARTZ Laboratory EA 7393, IUT of Montreuil- Paris8 University, 93100 Montreuil, France)

  • Sid-Ali Addouche

    (QUARTZ Laboratory EA 7393, IUT of Montreuil- Paris8 University, 93100 Montreuil, France)

  • Sofiene Dellagi

    (LGIPM, UFR MIM -Lorraine University, 57070 Lorraine, France)

  • Abderrahman El Mhamedi

    (QUARTZ Laboratory EA 7393, IUT of Montreuil- Paris8 University, 93100 Montreuil, France)

Abstract

One of the most innovative solutions treated in the literature in order to reduce the environmental impact of urban parcel delivery logistics is the use of drones for delivery on the last kilometer. Consequently, nowadays, the primary challenge is essentially related to the drones’ fleet sizing according to its means of support for the urban delivery of parcels. In this paper, we will discuss the issue of dimensioning from a forecast of deliveries of an urban perimeter, the size of the fleet, the stock of battery to dispose of and the strategy of battery charging. We will present an analytical model expressing the proposed problem of the optimal drones’ delivery mission taking into account the issues of autonomy and energy consumption related to the drone’s technical specification. According to the developed analytical model, two optimization policies will be proposed. The first policy consists of planning missions under reducing distance. The second policy tries to make a compromise between the distance and the number of drones. A case study will be presented in order to compare the two policies based on the overall cost of a plan. The main objective of the study is to create a decision-making tool for the design of a drone fleet in the case of forecast deliveries over a time horizon under operational constraints.

Suggested Citation

  • Asma Troudi & Sid-Ali Addouche & Sofiene Dellagi & Abderrahman El Mhamedi, 2018. "Sizing of the Drone Delivery Fleet Considering Energy Autonomy," Sustainability, MDPI, vol. 10(9), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3344-:d:170733
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    References listed on IDEAS

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    2. 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.
    3. Rave, Alexander & Fontaine, Pirmin & Kuhn, Heinrich, 2023. "Drone location and vehicle fleet planning with trucks and aerial drones," European Journal of Operational Research, Elsevier, vol. 308(1), pages 113-130.
    4. 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.
    5. Mengyuan Gou & Haiyan Yu, 2023. "Online Delivery Problem for Hybrid Truck–Drone System with Independent and Truck-Carried Drones," Sustainability, MDPI, vol. 15(2), pages 1-15, January.
    6. 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).
    7. Fang Li & Oliver Kunze, 2023. "A Comparative Review of Air Drones (UAVs) and Delivery Bots (SUGVs) for Automated Last Mile Home Delivery," Logistics, MDPI, vol. 7(2), pages 1-32, April.
    8. 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.
    9. Ming-fei Chen & Yan-qiu Liu & Yang Song & Qi Sun, 2019. "A Contract Coordination Model of Dual-Channel Delivery between UAVs and Couriers Considering the Uncertainty of Delivery for Last Mile," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-11, December.
    10. Grzegorz Radzki & Izabela Nielsen & Paulina Golińska-Dawson & Grzegorz Bocewicz & Zbigniew Banaszak, 2021. "Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments," Sustainability, MDPI, vol. 13(9), pages 1-23, May.
    11. Maria Elena Bruni & Sara Khodaparasti, 2022. "A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    12. Liu, Zeyu & Li, Xueping & Khojandi, Anahita, 2022. "The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    13. 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.
    14. Sergio Maria Patella & Gianluca Grazieschi & Valerio Gatta & Edoardo Marcucci & Stefano Carrese, 2020. "The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review," Sustainability, MDPI, vol. 13(1), pages 1-29, December.
    15. Mbiadou Saleu, Raïssa G. & Deroussi, Laurent & Feillet, Dominique & Grangeon, Nathalie & Quilliot, Alain, 2022. "The parallel drone scheduling problem with multiple drones and vehicles," European Journal of Operational Research, Elsevier, vol. 300(2), pages 571-589.
    16. Juntunen, Jouni K. & Martiskainen, Mari, 2021. "Improving understanding of energy autonomy: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).

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