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Potentialities of drones and ground autonomous delivery devices for last-mile logistics

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  • Lemardelé, Clément
  • Estrada, Miquel
  • Pagès, Laia
  • Bachofner, Mónika

Abstract

The e-commerce boom has increased the complexity of last-mile logistics operations in urban environments. In this context, unmanned aerial vehicles (UAVs), also known as delivery drones, and ground autonomous delivery devices (GADDs) show great potentialities. The objective of this paper is to provide strategic insights to adequately match these autonomous technologies with some given characteristics of cities and help define relevant decision variables. Using continuous approximation equations, the operations costs as well as the externalities induced by a) GADDs in association with an urban consolidation center (UCC) and b) truck-launched UAVs are estimated. Then, the developed mathematical formulations are applied in two different use cases: a part of the Paris suburbs (France) and the historical center of Barcelona (Spain). In less dense and larger service regions such as the Paris suburbs, truck-launched delivery drones seem more suitable to reduce the carriers’ operations costs. In denser neighborhoods such as the Barcelona historical center, GADDs are expected to be more economically profitable. In both use cases, GADDs would generate less externalities. Finally, considering the high uncertainty of some input parameters, a sensitivity analysis of the models is done.

Suggested Citation

  • Lemardelé, Clément & Estrada, Miquel & Pagès, Laia & Bachofner, Mónika, 2021. "Potentialities of drones and ground autonomous delivery devices for last-mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:transe:v:149:y:2021:i:c:s1366554521000995
    DOI: 10.1016/j.tre.2021.102325
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    Cited by:

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    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. 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).
    5. Zeng, Shouzhen & Zhang, Na & Zhang, Chonghui & Su, Weihua & Carlos, Llopis-Albert, 2022. "Social network multiple-criteria decision-making approach for evaluating unmanned ground delivery vehicles under the Pythagorean fuzzy environment," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Pahwa, Anmol & Jaller, Miguel, 2022. "A cost-based comparative analysis of different last-mile strategies for e-commerce delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    7. Srinivas, Sharan & Ramachandiran, Surya & Rajendran, Suchithra, 2022. "Autonomous robot-driven deliveries: A review of recent developments and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    8. He, Xinyu & He, Fang & Li, Lishuai & Zhang, Lei & Xiao, Gang, 2022. "A route network planning method for urban air delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    9. 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).
    10. Jaller, Miguel & Pahwa, Anmol, 2023. "Coping with the Rise of E-commerce Generated Home Deliveries through Innovative Last-mile Technologies and Strategies," Institute of Transportation Studies, Working Paper Series qt5t76x0kh, Institute of Transportation Studies, UC Davis.

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