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Design and Simulation of Logistics Network Model Based on Particle Swarm Optimization Algorithm.

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Summary
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This study optimizes logistics networks using a discrete particle swarm optimization (PSO) algorithm. The enhanced PSO model improves efficiency and reduces costs for e-commerce logistics.

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Area of Science:

  • Operations Research
  • Computer Science
  • Supply Chain Management

Background:

  • E-commerce growth necessitates efficient logistics and express services.
  • Optimizing logistics networks reduces route waste and transportation costs.
  • Hub-and-spoke network models are crucial for efficient logistics.

Purpose of the Study:

  • To develop an optimized logistics network model.
  • To minimize connection distances and total costs between hubs.
  • To enhance the efficiency and cost-effectiveness of logistics operations.

Main Methods:

  • Constructed a hub-and-spoke network (HSN) model.
  • Designed a discrete particle swarm optimization (PSO) algorithm.
  • Improved the PSO algorithm with an Exchange() function for enhanced search strategy.

Main Results:

  • The improved double-layer discrete PSO algorithm effectively solves the logistics network model.
  • The optimized PSO algorithm demonstrates faster convergence and higher precision.
  • Application of the model integrates logistics resources and reduces overall costs.

Conclusions:

  • The enhanced PSO algorithm offers a superior approach for logistics network optimization.
  • Implementing optimized logistics networks leads to significant cost reductions and efficiency gains.
  • This research contributes to more sustainable and cost-effective e-commerce logistics solutions.