Research on Cold Chain Logistics Distribution Path Optimization of Fresh E-commerce Considering Carbon Cost and Time Window
Abstract
Driven by the booming development of digital retail, fresh food e-commerce has expanded rapidly in recent years, while its supporting cold chain logistics system still faces prominent operational problems, including unreasonable route planning, high comprehensive distribution costs, excessive product spoilage, and substantial carbon emissions. To address the multi-constraint and multi-objective optimization characteristics of fresh food last-mile distribution, this study constructs a comprehensive cold chain distribution path optimization model that integrates transportation cost, perishable loss cost, hybrid time window penalty cost, and carbon emission cost. On this basis, an improved adaptive genetic algorithm (IAGA) is proposed to solve the established model, which dynamically adjusts crossover and mutation probabilities during iterations and effectively overcomes the premature convergence and local optimal defects of the traditional genetic algorithm (TGA). Numerical simulation and comparative experiments based on real-world fresh e-commerce distribution scenarios are conducted. The results demonstrate that the proposed model and algorithm can significantly reduce total distribution costs, cut carbon emissions, and mitigate time penalty losses, thereby improving the overall operational efficiency and low-carbon sustainability of cold chain distribution systems. This research provides a reliable theoretical reference and practical operational strategy for refined and green path scheduling management of fresh cold chain logistics enterprises.