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Updated: Sep 13, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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Order fulfillment planning with crowdsourcing integration.

Xuejing Cao1, Zhongmin Da2, Xuefei Wang3

  • 1School of Business, Shaoxing University, Shaoxing, 312000, China.

Scientific Reports
|July 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for e-commerce delivery, the Two-Echelon Multi-Depot Crowdsourcing Split Delivery Order Fulfillment Problem. It uses crowdsourced agents and split deliveries to cut logistics costs and boost flexibility.

Keywords:
Adaptive large neighborhood searchCrowdsourcingE-commerceOrder fulfillmentSplit delivery

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Area of Science:

  • Operations Research
  • Logistics Management
  • E-commerce Optimization

Background:

  • Rapid e-commerce growth presents significant last-mile delivery challenges.
  • Urban logistics increasingly leverages crowdsourcing for flexible delivery solutions.
  • Traditional delivery models struggle with the scale and demands of modern online retail.

Purpose of the Study:

  • To formally define the Two-Echelon Multi-Depot Crowdsourcing Split Delivery Order Fulfillment Problem (2E-MDCSDOFP).
  • To develop an efficient algorithm for solving this complex logistics problem.
  • To evaluate the cost-effectiveness and flexibility improvements offered by crowdsourced split deliveries.

Main Methods:

  • Integer programming formulation for minimizing total logistics cost.
  • Development of an adaptive large neighborhood search (ALNS) algorithm.
  • Extensive experimental validation on benchmark scenarios.

Main Results:

  • The proposed 2E-MDCSDOFP model effectively integrates crowdsourcing and split deliveries.
  • The ALNS algorithm demonstrates efficiency in solving large-scale instances.
  • Significant improvements in delivery flexibility and cost-effectiveness were observed compared to traditional methods.

Conclusions:

  • Crowdsourcing and split deliveries offer a valuable approach to enhance modern distribution systems.
  • The developed model and algorithm provide a robust solution for complex e-commerce fulfillment.
  • This research highlights a pathway to more efficient and adaptable urban logistics.