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Drug Distribution: Volume of Distribution01:25

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Related Experiment Video

Updated: Feb 13, 2026

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing
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Distribution path robust optimization of electric vehicle with multiple distribution centers.

Changxi Ma1, Wei Hao2, Ruichun He1

  • 1School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, Gansu, China.

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|March 9, 2018
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Summary

This study develops a robust optimization model and algorithm for electric vehicle (EV) distribution, ensuring reliable routes with multiple centers and charging stations. The enhanced genetic algorithm provides detailed, road-by-road schemes for efficient EV logistics.

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Area of Science:

  • Operations Research
  • Logistics and Supply Chain Management
  • Transportation Engineering

Background:

  • Optimizing electric vehicle (EV) distribution is crucial for efficiency and robustness.
  • Existing models often lack detailed road-by-road schemes and insensitivity to uncertainty.
  • The need for robust distribution paths considering multiple centers and charging infrastructure is increasing.

Purpose of the Study:

  • To establish a robust optimization model for electric vehicle (EV) distribution paths.
  • To achieve high robustness and insensitivity to uncertainty factors in EV distribution.
  • To develop detailed, road-by-road distribution schemes with multiple distribution centers and charging facilities.

Main Methods:

  • Development of a robust optimization model for EV distribution paths based on Bertsimas' theory.
  • Incorporation of adjustable robustness and minimum transport time as the optimization objective.
  • Implementation of an enhanced three-segment genetic algorithm with specific coding, decoding, and evolutionary strategies to solve the model.

Main Results:

  • The robust optimization model successfully identifies highly robust EV distribution paths.
  • The enhanced genetic algorithm effectively solves the complex distribution problem, generating detailed road-by-road schemes.
  • The model and algorithm were validated using a real-world road network example, demonstrating practical applicability.

Conclusions:

  • The proposed robust optimization model provides a reliable method for planning EV distribution networks.
  • The enhanced genetic algorithm ensures the generation of optimal and feasible distribution schemes.
  • This approach enables more robust electric vehicle distribution with multiple centers, considering charging infrastructure and minimizing transport time.