A Low-Carbon Scheduling Method for Container Intermodal Transport Using an Improved Grey Wolf–Harris Hawks Hybrid Algorithm

Container intermodal scheduling is critical for advancing low-carbon logistics within inland port systems. However, the scheduling process faces several challenges, including the complexity of coordinating transport modes and complying with carbon emission policies. To address these issues, this study proposes a multi-objective optimization model that simultaneously considers transportation cost, carbon emissions, and time efficiency under soft time window constraints. The model is solved using an improved grey wolf–Harris hawks hybrid algorithm (IGWOHHO). This algorithm enhances population diversity through Tent chaotic mapping, balances global exploration and local exploitation with adaptive weight adjustment, and improves solution quality by incorporating an elite retention strategy. Benchmark tests show that IGWOHHO outperforms several well-established metaheuristic algorithms in terms of convergence accuracy and robustness. A case study based on an intermodal transport network further demonstrates that adjusting the objective weights flexibly provides decision support under various scenarios, achieving a dynamic balance between cost, efficiency, and environmental impact. Additionally, the analysis reveals that appropriate carbon tax pricing can encourage the adoption of greener transport modes, promoting the sustainable development of multimodal logistics systems.