Optimization of Cold Chain Distribution Route with Mixed Time Window considering Customer Priority.

Area of Science:

• Operations Research
• Logistics Management
• Supply Chain Optimization

Background:

• Vehicle routing problems with time windows are complex, especially with mixed time windows and customer prioritization.
• Cold chain logistics requires efficient routing to minimize costs and ensure product quality.

Purpose of the Study:

• To develop a customer differentiation management strategy for mixed time window vehicle routing optimization.
• To solve the cold chain distribution route optimization problem considering customer priority.

Main Methods:

• Established a comprehensive customer priority evaluation index.
• Applied DBSCAN clustering algorithm for customer priority analysis.
• Constructed fuzzy time windows using trapezoidal fuzzy numbers.
• Developed a mathematical programming model minimizing total costs (fixed, green, penalty, refrigeration, cargo damage).
• Utilized an improved genetic algorithm for model solving.

Main Results:

• The proposed method effectively solves the routing problem for refrigerated trucks with customer priority.
• Demonstrated the model's rationality through a case study of imported fruit distribution.
• Achieved optimization considering mixed time windows and customer importance.

Conclusions:

• The customer priority strategy provides a novel approach for refrigerated truck routing optimization.
• Findings offer practical insights for improving cold chain logistics efficiency and customer satisfaction.