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Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural

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Area of Science:

  • Operations Research
  • Transportation Engineering
  • Computer Science

Background:

  • Ensuring safety in hazardous materials transportation requires effective route optimization.
  • Road screening is a critical prerequisite for optimizing hazardous materials distribution routes to mitigate security risks.

Purpose of the Study:

  • To develop an efficient road screening algorithm for hazardous materials transportation.
  • To construct a multi-objective robust optimization model for minimizing transportation risk and time.
  • To design a multi-objective genetic algorithm for solving the robust optimization problem.

Main Methods:

  • A road screening algorithm integrating genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) was developed using 15 road network attributes.
  • A multi-objective robust optimization model with adjustable robustness was formulated for single distribution center scenarios.
  • A specialized multi-objective genetic algorithm with improved selection, crossover, mutation, and Pareto solution construction strategies was employed.

Main Results:

  • The GA-LM-NN algorithm rapidly identifies suitable road sets for hazardous materials transportation.
  • The multi-objective robust optimization model and algorithm efficiently generate Pareto optimal solutions for distribution routes with varying robustness levels.

Conclusions:

  • The proposed GA-LM-NN road screening algorithm enhances the safety of hazardous materials transportation.
  • The developed robust optimization framework provides effective solutions for minimizing risk and time in hazardous materials logistics.