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Crowdsourcing mode evaluation for parcel delivery service platforms

Author

Listed:
  • Zhen, Lu
  • Wu, Yiwei
  • Wang, Shuaian
  • Yi, Wen

Abstract

The fast-growing practice of e-commerce implies a strong increase in the urban parcel delivery, which in turn creates significant pressure on last-mile city logistics. Because the crowdsourced delivery offers greater flexibility and requires less capital investment than traditional delivery methods, it has been playing a more crucial role when faced with the growing demand for the urban parcel delivery. With the increasing maturity of the crowdsourced delivery and the fierce competition among platforms, the evaluation of different crowdsourcing modes for the urban parcel delivery becomes important. This study proposes six mathematical models to evaluate different operation modes of the crowdsourced delivery in a quantitative way. Several realistic factors, such as the latest service time for each task, task cancellation rate and range distribution of tasks, are also analyzed in this paper. Numerical experiments are conducted to validate the effectiveness of the proposed models and to analyze the impact of different modes. Some managerial implications are also outlined on the basis of the numerical experiments and sensitivity analysis to help crowdsourced companies to make scientific decisions.

Suggested Citation

  • Zhen, Lu & Wu, Yiwei & Wang, Shuaian & Yi, Wen, 2021. "Crowdsourcing mode evaluation for parcel delivery service platforms," International Journal of Production Economics, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:proeco:v:235:y:2021:i:c:s0925527321000438
    DOI: 10.1016/j.ijpe.2021.108067
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    Cited by:

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    3. Xiao, Haohan & Xu, Min & Wang, Shuaian, 2023. "A game-theoretic model for crowd-shipping operations with profit improvement strategies," International Journal of Production Economics, Elsevier, vol. 262(C).
    4. He, Shan & Dai, Ying & Ma, Zu-Jun, 2023. "To offer or not to offer? The optimal value-insured strategy for crowdsourced delivery platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    5. Agnieszka Deja & Wojciech Ślączka & Magdalena Kaup & Jacek Szołtysek & Lyudmyla Dzhuguryan & Tygran Dzhuguryan, 2024. "Supply Chain Management in Smart City Manufacturing Clusters: An Alternative Approach to Urban Freight Mobility with Electric Vehicles," Energies, MDPI, vol. 17(21), pages 1-27, October.
    6. Chen, Enming & Zhou, Zhongbao & Li, Ruiyang & Chang, Zhongxiang & Shi, Jianmai, 2024. "The multi-fleet delivery problem combined with trucks, tricycles, and drones for last-mile logistics efficiency requirements under multiple budget constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 187(C).
    7. Yang, Xuan & Kong, Xiang T.R. & Huang, George Q., 2024. "Synchronizing crowdsourced co-modality between passenger and freight transportation services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    8. Ghaderi, Hadi & Zhang, Lele & Tsai, Pei-Wei & Woo, Jihoon, 2022. "Crowdsourced last-mile delivery with parcel lockers," International Journal of Production Economics, Elsevier, vol. 251(C).

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