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This study introduces the EY model, combining genetic EM algorithm and Bayesian networks (BN), to effectively reduce logistics delays. The model achieves 98% accuracy, improving efficiency in e-commerce logistics.

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Area of Science:

  • Operations Research
  • Computer Science
  • Supply Chain Management

Background:

  • E-commerce growth intensifies logistics demands, making delays a critical concern.
  • Existing algorithms struggle with complex, multi-node logistics challenges.
  • Probabilistic uncertainty in logistics requires advanced modeling techniques.

Purpose of the Study:

  • To develop a novel algorithm model for controlling and reducing logistics delays.
  • To investigate the probabilistic factors contributing to logistics disruptions.
  • To enhance the efficiency and accuracy of logistics delay prediction.

Main Methods:

  • Construction of the EY model, integrating a genetic EM algorithm with a Bayesian network (BN).
  • Application of iterative optimization strategies for complex travel problems.
  • Simulation experiments to validate the model's performance and accuracy.

Main Results:

  • The EY model demonstrates significantly improved calculation efficiency.
  • Achieved a high actuarial accuracy rate of 98% in predicting logistics delays.
  • Verified the model's feasibility and effectiveness in controlling logistics delays.

Conclusions:

  • The EY model offers a robust solution for mitigating logistics delays in e-commerce.
  • The integration of genetic EM algorithm and BN enhances predictive accuracy.
  • This approach provides a valuable tool for optimizing logistics operations.