Document Open Access Logo

Crowdsourced Delivery with Drones in Last Mile Logistics

Authors Mehdi Behroozi , Dinghao Ma

PDF

File

Thumbnail PDF
PDF
OASIcs.ATMOS.2020.17.pdf
  • Filesize: 0.92 MB
  • 12 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
Keywords
  • Last-mile delivery
  • Drone delivery
  • Sharing Economy

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

We consider a combined system of regular delivery trucks and crowdsourced drones to provide a technology-assisted crowd-based last-mile delivery experience. We develop analytical models and methods for a system in which package delivery is performed by a big truck carrying a large number of packages to a neighborhood or a town in a metropolitan area and then assign the packages to crowdsourced drone operators to deliver them to their final destinations. A combination of heuristic algorithms is used to solve this NP-hard problem, computational results are presented, and an exhaustive sensitivity analysis is done to check the influence of different parameters and assumptions.

Cite As Get BibTex

Mehdi Behroozi and Dinghao Ma. Crowdsourced Delivery with Drones in Last Mile Logistics. In 20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2020). Open Access Series in Informatics (OASIcs), Volume 85, pp. 17:1-17:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/OASIcs.ATMOS.2020.17

Author Details

Mehdi Behroozi
  • Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA
Dinghao Ma
  • Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA

Funding

Authors thank the National Science Foundation and Northeastern University for supporting this research.
  • Behroozi, Mehdi: Authors thank the National Science Foundation and Northeastern University for supporting this research.

Acknowledgements

Authors would like to thank Northeastern University’s SHARE Group for creating an environment in which this research could be accomplished.

References

  1. Amazon. About Amazon Flex. https://www.cnbc.com/2018/10/02/amazon-raises-minimum-wage-to-15-for-all-us-employees.html. Accessed: 30-November-2018.
  2. Amazon. Amazon raises minimum wage to $15 for all us employees. https://flex.amazon.com/about. Accessed: 30-November-2018.
  3. John Gunnar Carlsson, Mehdi Behroozi, Raghuveer Devulapalli, and Xiangfei Meng. Household-level economies of scale in transportation. Operations Research, 64(6):1372-1387, 2016. Google Scholar
  4. P Chen and S. M Chankov. Crowdsourced delivery for last-mile distribution: An agent-based modelling and simulation approach. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), volume 2017-, pages 1271-1275. IEEE, 2017. Google Scholar
  5. Katarzyna Gdowska, Ana Viana, and JoãO Pedro Pedroso. Stochastic last-mile delivery with crowdshipping. Transportation Research Procedia, 30:90-100, 2018. Google Scholar
  6. Roel Gevaers, Eddy Van de Voorde, and Thierry Vanelslander. Characteristics and typology of last-mile logistics from an innovation perspective in an urban context. City Distribution and Urban Freight Transport: Multiple Perspectives, Edward Elgar Publishing, pages 56-71, 2011. Google Scholar
  7. Martin Joerss, Jürgen Schröder, Florian Neuhaus, Christoph Klink, and Florian Mann. Parcel delivery: The future of last mile. McKinsey & Company, 2016. Google Scholar
  8. Alp M. Arslan, Niels Agatz, Leo Kroon, and Rob Zuidwijk. Crowdsourced delivery—a dynamic pickup and delivery problem with ad hoc drivers. Transportation Science, 53(1):222-235, 2018. Google Scholar
  9. John Miller, Yu Nie, and Amanda Stathopoulos. Crowdsourced urban package delivery: Modeling traveler willingness to work as crowdshippers. Transportation Research Record, 2610(1):67-75, 2017. Google Scholar
  10. Victor Paskalathis and Azhari Sn. Ant colony optimization on crowdsourced delivery trip consolidation. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 11(2):109-118, 2017. URL: https://doaj.org/article/34836288983f4d2ba83accf30013a834.
  11. A Punel and A Stathopoulos. Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Research Part E-Logistics And Transportation Review, 105:18-38, 2017. Google Scholar
  12. Afonso Sampaio, Martin Savelsbergh, Lucas Veelenturf, and Tom van Woensel. Crowd-Based City Logistics. Decision-Making Models and Solutions, 2019. Google Scholar
  13. Qi Wei, Li Lefei, Liu Sheng, and Shen Zuo-Jun Max. Shared mobility for last-mile delivery: Design, operational prescriptions, and environmental impact. Manufacturing & Service Operations Management, 20(4):737-751, 2018. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact