Optimization of Multimodal Transportation Routes for North-to-South Grain Transportation in China Considering Carbon Emissions

This study presents a multi-objective optimization framework for China’s North-to-South Grain Transportation (NSGT), balancing costs, time, carbon emissions, and grain quality loss to promote sustainable logistics. We propose a hybrid algorithm combining genetic optimization with reinforcement learning to identify efficient routes and evaluate trade-offs. Compared to standard methods, our approach achieves better solution diversity and robustness, as validated by sensitivity analysis, scalability tests, and statistical comparisons. The findings advance carbon accounting in multimodal transport and provide practical guidance for policymakers to enhance eco-friendly grain distribution.