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Crowdsourced Delivery: A Dynamic Pickup and Delivery Problem with Ad-hoc Drivers

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  • Arslan, A.M.
  • Agatz, N.A.H.
  • Kroon, L.G.
  • Zuidwijk, R.A.

Abstract

The trend towards shorter delivery lead-times reduces operational efficiency and increases transportation costs for internet retailers. Mobile technology, however, creates new opportunities to organize the last-mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place to make deliveries. We consider a peer-to-peer platform that automatically creates matches between parcel delivery tasks and ad-hoc drivers. The platform also operates a fleet of backup vehicles to serve the tasks that cannot be served by the ad-hoc drivers. The matching of tasks, drivers and backup vehicles gives rise to a new variant of the dynamic pick-up and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the various subproblems. In order to investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the environment and the behavior of the ad-hoc drivers. The results suggest that the use of ad-hoc drivers has the potential to make the last-mile more cost-efficient and can reduce the system-wide vehicle-miles.

Suggested Citation

  • Arslan, A.M. & Agatz, N.A.H. & Kroon, L.G. & Zuidwijk, R.A., 2016. "Crowdsourced Delivery: A Dynamic Pickup and Delivery Problem with Ad-hoc Drivers," ERIM Report Series Research in Management ERS-2016-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:79743
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    References listed on IDEAS

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    Cited by:

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    2. Boysen, Nils & Schwerdfeger, Stefan & Weidinger, Felix, 2018. "Scheduling last-mile deliveries with truck-based autonomous robots," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1085-1099.
    3. Bhoopalam, Anirudh Kishore & Agatz, Niels & Zuidwijk, Rob, 2018. "Planning of truck platoons: A literature review and directions for future research," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 212-228.
    4. Agnieszka Szmelter-Jarosz & Jagienka Rześny-Cieplińska, 2019. "Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    5. Behrend, Moritz & Meisel, Frank, 2018. "The integration of item-sharing and crowdshipping: Can collaborative consumption be pushed by delivering through the crowd?," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 227-243.
    6. Ermagun, Alireza & Stathopoulos, Amanda, 2018. "To bid or not to bid: An empirical study of the supply determinants of crowd-shipping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 468-483.
    7. Alireza Ermagun & Ali Shamshiripour & Amanda Stathopoulos, 2020. "Performance analysis of crowd-shipping in urban and suburban areas," Transportation, Springer, vol. 47(4), pages 1955-1985, August.
    8. Stacy A. Voccia & Ann Melissa Campbell & Barrett W. Thomas, 2019. "The Same-Day Delivery Problem for Online Purchases," Service Science, INFORMS, vol. 53(1), pages 167-184, February.
    9. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.
    10. Le, Tho V. & Ukkusuri, Satish V. & Xue, Jiawei & Van Woensel, Tom, 2021. "Designing pricing and compensation schemes by integrating matching and routing models for crowd-shipping systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    11. Wenyi Chen & Martijn Mes & Marco Schutten, 2018. "Multi-hop driver-parcel matching problem with time windows," Flexible Services and Manufacturing Journal, Springer, vol. 30(3), pages 517-553, September.
    12. Tao Yang & Weixin Wang, 2022. "Logistics Network Distribution Optimization Based on Vehicle Sharing," Sustainability, MDPI, vol. 14(4), pages 1-12, February.
    13. Cleophas, Catherine & Cottrill, Caitlin & Ehmke, Jan Fabian & Tierney, Kevin, 2019. "Collaborative urban transportation: Recent advances in theory and practice," European Journal of Operational Research, Elsevier, vol. 273(3), pages 801-816.
    14. Allahviranloo, Mahdieh & Baghestani, Amirhossein, 2019. "A dynamic crowdshipping model and daily travel behavior," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 175-190.

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    Keywords

    crowdsourced delivery; pickup and delivery problem; ad-hoc drivers;
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