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Optimization of frozen goods distribution logistics network based on k-means algorithm and priority classification.

Jianli Shi1

  • 1School of Management Science and engineering, Chongqing Technology and Business University, Chongqing, Chongqing, 400061, China. shjl20043528@163.com.

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Summary

This study introduces a machine learning approach to optimize cold chain logistics for frozen goods, significantly cutting costs and reducing waste. The method enhances efficiency in distribution networks through intelligent demand prediction and resource allocation.

Keywords:
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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Machine Learning

Background:

  • Maintaining frozen goods quality requires efficient cold chain logistics.
  • Existing systems face challenges in cost and resource utilization.

Purpose of the Study:

  • To develop a machine learning-based method for optimizing cold chain logistics networks for frozen goods.
  • To reduce costs and improve resource utilization in frozen goods distribution.

Main Methods:

  • Utilized K-means clustering for geographical seller grouping.
  • Employed Gaussian Process Regression for demand prediction.
  • Applied the Capuchin Search Algorithm for simultaneous optimization of distributor location and resource allocation.

Main Results:

  • Achieved a 34.76% reduction in overall costs.
  • Reduced resource wastage by 15.6% compared to existing systems.
  • Demonstrated a multi-objective optimization approach.

Conclusions:

  • The proposed machine learning method offers a valuable tool for optimizing frozen goods distribution.
  • The approach provides managerial insights and reduces complexity in cold chain logistics.
  • Highlights the benefits of demand prediction and integrated optimization.