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<p>E-commerce has experienced rapid growth over the past decade, creating new challenges for last-mile and same-day delivery services. In response to the explosive increase in online orders and the emerging role of crowdsourcing in urban logistics, we introduce and formally define the Two-Echelon Multi-Depot Crowdsourcing Split Delivery Order Fulfillment Problem (2E-MDCSDOFP). This problem extends traditional two-echelon vehicle routing by integrating a crowdsourced delivery mechanism in the second echelon, allowing independent agents to perform customer deliveries. Furthermore, we relax the conventional constraint that each customer is served only once, enabling split deliveries to better utilize flexible and heterogeneous crowdsourced resources. We formulate the problem as a pure integer program aimed at minimizing the total logistics cost. To tackle the computational complexity, we develop an adaptive large neighborhood search algorithm tailored to exploit the problem structure and handle large-scale instances efficiently. Experimental results on extensive benchmark scenarios demonstrate that our approach significantly improves delivery flexibility and cost-effectiveness compared to traditional methods, highlighting the value of crowdsourcing in modern distribution systems.</p>

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Order fulfillment planning with crowdsourcing integration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月26日

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  • Xuejing Cao,
  • Zhongmin Da,
  • Xuefei Wang

摘要

E-commerce has experienced rapid growth over the past decade, creating new challenges for last-mile and same-day delivery services. In response to the explosive increase in online orders and the emerging role of crowdsourcing in urban logistics, we introduce and formally define the Two-Echelon Multi-Depot Crowdsourcing Split Delivery Order Fulfillment Problem (2E-MDCSDOFP). This problem extends traditional two-echelon vehicle routing by integrating a crowdsourced delivery mechanism in the second echelon, allowing independent agents to perform customer deliveries. Furthermore, we relax the conventional constraint that each customer is served only once, enabling split deliveries to better utilize flexible and heterogeneous crowdsourced resources. We formulate the problem as a pure integer program aimed at minimizing the total logistics cost. To tackle the computational complexity, we develop an adaptive large neighborhood search algorithm tailored to exploit the problem structure and handle large-scale instances efficiently. Experimental results on extensive benchmark scenarios demonstrate that our approach significantly improves delivery flexibility and cost-effectiveness compared to traditional methods, highlighting the value of crowdsourcing in modern distribution systems.