Multi-objective optimization of cold chain distribution routes considering traffic congestion

This study presents an advanced multi-objective optimization model for cold chain distribution (CCD) that explicitly accounts for the impact of traffic congestion on key cost components. By integrating fixed costs, transportation costs, refrigeration costs, and carbon emission costs, along with customer satisfaction, the model aims to minimize total distribution costs while satisfying both environmental and operational constraints. An improved genetic algorithm (I-GA) is applied to optimize CCD routes under these constraints. Simulation results demonstrate that the I-GA significantly outperforms the traditional genetic algorithm (T-GA) in terms of the number of vehicles used and total travel distance. Notably, although incorporating traffic congestion into the model increases the overall CCD cost by 7.06%, it concurrently reduces carbon emission costs by 3.72%. Furthermore, the study identifies a synergistic effect when optimizing refrigeration costs and carbon emission costs jointly: this dual optimization results in only minimal increases in overall cost. This research provides a valuable decision-support tool for enterprises to develop more efficient, sustainable, and profitable CCD strategies. • Developed a multi-objective optimization model to minimize total CCD cost. • Improved GA (I-GA) outperforms traditional GA in route optimization. • Traffic congestion increases costs but optimization cuts emission costs.