Multiobjective optimization of vehicle multimodal transport paths considering carbon emissions: a hybrid genetic-tabu search approach

This study focuses on the optimization of multimodal transportation routes for commercial vehicles, constructing a multi-objective optimization model considering carbon emissions, transportation costs, etc., and designing a genetic taboo search hybrid algorithm (GA-TS) to solve it. Taking the transportation network in the Yangtze River Basin as a case study, compare the optimal path and cost under carbon trading and carbon tax policies. The results indicate that the optimal path under carbon trading policy is "Chongqing→Jingzhou→Wuhan→Huangshi→Anqing→Shanghai", using rail public water intermodal transportation; Under the carbon tax policy, the carbon emissions of railway transportation throughout the entire journey have been reduced, but the proportion of transportation costs is high. Extending the time window can reduce costs, but excessively relaxing or increasing storage costs. The increase in carbon prices or tax rates promotes enterprises to shift towards low-carbon transportation, and research has verified the efficiency of the GA-TS algorithm, providing reference for the industry's green transformation.