Distribution path robust optimization of electric vehicle with multiple distribution centers.

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.