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Joint optimization of parcel allocation and crowd routing for crowdsourced last-mile delivery

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  • Wang, Li
  • Xu, Min
  • Qin, Hu

Abstract

Urban last-mile delivery providers are facing more and more challenges with the explosive development of e-commerce. The advancement of smart mobile and communication technology in recent years has stimulated the development of a new business model of city logistics, referred to as crowdsourced delivery or crowd-shipping. In this paper, we investigate a form of crowdsourced last-mile delivery that utilizes the journeys of commuters/travelers (crowd-couriers) to deliver parcels from intermediate stations to customers. We consider a logistics service provider that jointly optimizes parcel allocation to intermediate stations and the delivery routing of the crowd-couriers. The joint optimization model gives rise to a new variant of the last-mile delivery problem. We propose a data-driven column generation algorithm to solve the problem based on a set-partitioning formulation. Additionally, a rolling-horizon approach is proposed to address large-scale instances. Extensive numerical experiments are conducted to verify the efficiency of our model and solution approach, as well as the significance of the joint optimization of parcel allocation and the delivery route of the crowdsourced last-mile delivery. The results show that our data-driven column generation algorithm can obtain (near-)optimal solutions for up to 200 parcels in significantly less time than the exact algorithm. For larger instances, the combination of the data-driven column generation algorithm and the rolling-horizon approach can obtain good-quality solutions for up to 1000 parcels in 15 min. Moreover, compared with crowd-courier route optimization only, the joint optimization of parcel allocation and crowd-routing reduces the total cost by 32%.

Suggested Citation

  • Wang, Li & Xu, Min & Qin, Hu, 2023. "Joint optimization of parcel allocation and crowd routing for crowdsourced last-mile delivery," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 111-135.
    • Handle: RePEc:eee:transb:v:171:y:2023:i:c:p:111-135
    DOI: 10.1016/j.trb.2023.03.007
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    References listed on IDEAS

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    1. Yu, Vincent F. & Jodiawan, Panca & Redi, A.A.N. Perwira, 2022. "Crowd-shipping problem with time windows, transshipment nodes, and delivery options," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    2. Perboli, Guido & Brotcorne, Luce & Bruni, Maria Elena & Rosano, Mariangela, 2021. "A new model for Last-Mile Delivery and Satellite Depots management: The impact of the on-demand economy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    3. repec:inm:orijoo:v:4:y:2022:i:3:p:278-303 is not listed on IDEAS
    4. Sheng Liu & Long He & Zuo-Jun Max Shen, 2021. "On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors," Management Science, INFORMS, vol. 67(7), pages 4095-4119, July.
    5. Carlos F. Daganzo, 2005. "Logistics Systems Analysis," Springer Books, Springer, edition 0, number 978-3-540-27516-9, November.
    6. Ahamed, Tanvir & Zou, Bo & Farazi, Nahid Parvez & Tulabandhula, Theja, 2021. "Deep Reinforcement Learning for Crowdsourced Urban Delivery," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 227-257.
    7. 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.
    8. 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.
    9. Nieto-Isaza, Santiago & Fontaine, Pirmin & Minner, Stefan, 2022. "The value of stochastic crowd resources and strategic location of mini-depots for last-mile delivery: A Benders decomposition approach," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 62-79.
    10. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2021. "Crowdsourced delivery: A review of platforms and academic literature," Omega, Elsevier, vol. 98(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. Çavdar, Bahar & Sokol, Joel, 2015. "A distribution-free TSP tour length estimation model for random graphs," European Journal of Operational Research, Elsevier, vol. 243(2), pages 588-598.
    13. Mancini, Simona & Gansterer, Margaretha, 2022. "Bundle generation for last-mile delivery with occasional drivers," Omega, Elsevier, vol. 108(C).
    14. Max Shen, Zuo-Jun & Qi, Lian, 2007. "Incorporating inventory and routing costs in strategic location models," European Journal of Operational Research, Elsevier, vol. 179(2), pages 372-389, June.
    15. Archetti, Claudia & Savelsbergh, Martin & Speranza, M. Grazia, 2016. "The Vehicle Routing Problem with Occasional Drivers," European Journal of Operational Research, Elsevier, vol. 254(2), pages 472-480.
    16. Iman Dayarian & Martin Savelsbergh, 2020. "Crowdshipping and Same‐day Delivery: Employing In‐store Customers to Deliver Online Orders," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2153-2174, September.
    17. Kafle, Nabin & Zou, Bo & Lin, Jane, 2017. "Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 62-82.
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    2. Xiao, Haohan & Xu, Min & Wang, Shuaian, 2023. "Crowd-shipping as a Service: Game-based operating strategy design and analysis," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).

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