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Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm.

Binbin Liu1,2

  • 1Department of Business Administration, Suqian University, Suqian 223800, China.

Computational Intelligence and Neuroscience
|November 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a recursive fuzzy neural network algorithm for optimizing e-commerce logistics distribution paths. The intelligent algorithm enhances distribution accuracy to over 97%, ensuring efficient route selection.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • E-commerce logistics distribution path optimization faces challenges in scientific guarantee for optimal scheme determination.
  • Intelligent algorithms are crucial for achieving optimized and adaptive distribution path settings.

Purpose of the Study:

  • To develop and evaluate a logistics distribution path optimization model for e-commerce using a recursive fuzzy neural network algorithm.
  • To address the difficulties in scientifically determining optimal distribution paths in e-commerce logistics.

Main Methods:

  • Analysis of the current research status of logistics distribution path determination schemes.
  • Application of the recursive fuzzy neural network algorithm to e-commerce logistics distribution path scheme selection.

Main Results:

  • The recursive fuzzy neural network algorithm effectively optimizes e-commerce logistics distribution paths.
  • The algorithm enables the creation of optimal distribution routes based on route characteristic differences.
  • Achieved a distribution accuracy exceeding 97%.

Conclusions:

  • The recursive fuzzy neural network algorithm provides a scientifically sound method for optimizing e-commerce logistics distribution paths.
  • This approach enhances the efficiency and accuracy of distribution route selection in e-commerce.