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Area of Science:

  • Operations Research
  • Computer Science

Background:

  • Traditional logistic distribution methods face challenges in optimizing complex delivery plans.
  • Genetic algorithms offer a potential solution but require refinement for efficiency.

Purpose of the Study:

  • To improve logistic distribution methods using a refined genetic algorithm.
  • To optimize delivery area division and formulate functional delivery plans.

Main Methods:

  • Developed an improved genetic algorithm incorporating delivery area division.
  • Integrated parameters such as weight, time, customer value, and process indicators.
  • Simulated simple and soft time-limited logistic distribution models.

Main Results:

  • The improved genetic algorithm found optimal solutions in under 2 minutes.
  • Traditional methods required 4-5 minutes for optimization.
  • Demonstrated the effectiveness of the enhanced algorithm in logistic transportation allocation.

Conclusions:

  • The improved genetic algorithm significantly outperforms traditional methods in speed and efficiency.
  • This optimization method is effective for logistic distribution and transportation allocation.