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Research on Express Crowdsourcing Task Allocation Considering Distribution Mode under Customer Classification

Author

Listed:
  • Xiaohu Xing

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Chang Sun

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Xinqiang Chen

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

Abstract

In order to promote the sustainable development of crowdsourcing logistics and control the cost of crowdsourcing logistics while improving the quality of crowdsourcing services, this paper proposes a courier crowdsourcing task allocation model that considers delivery methods under customer classification, with the optimization objective of minimizing the total cost of the crowdsourcing platform. This model adopts two delivery modes: home delivery by crowdsource couriers and pickup by customers. Customers can freely choose the express delivery method according to their actual situation when placing orders, thus better meeting their needs. Based on the customer’s historical express-consumption data, the entropy weight RFM model is used to classify them, and different penalty functions are constructed for different categories of customers to reduce the total delivery cost and improve the on-time delivery of efficient and potential customers. And a Customer Classification Genetic Algorithm (CCGA) was designed for simulation experiments, which showed that the algorithm proposed in this study significantly improved the local search ability, thereby optimizing the delivery task path of express crowdsourcing. This improvement not only improves the delivery timeliness for efficient and potential customers, but also effectively reduces the total delivery cost. Therefore, the research on parcel crowdsourcing task allocation based on customer classification reduces the cost of crowdsourcing delivery platforms and improves customer satisfaction, which has certain theoretical research value and practical-application significance.

Suggested Citation

  • Xiaohu Xing & Chang Sun & Xinqiang Chen, 2024. "Research on Express Crowdsourcing Task Allocation Considering Distribution Mode under Customer Classification," Sustainability, MDPI, vol. 16(18), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7936-:d:1475975
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    References listed on IDEAS

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    1. 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.
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