## 1 Introduction  
Carbon emissions pose a significant global challenge that hinders sustainable development across various sectors. In response, many countries have set carbon neutrality targets and integrated low-carbon strategies into their national development frameworks. The transportation sector, being one of the largest contributors to greenhouse gas emissions, plays a crucial role in achieving carbon reduction targets. Moreover, global events, such as the COVID-19 pandemic, trade tensions, and the Russia-Ukraine conflict, have disrupted international supply chains. These disruptions have highlighted the need for logistics systems that are resilient, economically efficient, and environmentally sustainable. Container intermodal transport has emerged as a promising solution, integrating multiple transportation modes, such as roads, rail, and waterways. This approach enhances operational efficiency, reduces logistics costs, and lowers carbon emissions. However, scheduling such systems remains a complex task. It requires precise coordination among transport modes, adherence to time window constraints, and compliance with environmental regulations. Inland ports, which function as key hubs linking seaports to hinterland regions, have significant potential to advance low-carbon logistics by enhancing container transport efficiency and enabling multimodal coordination. Figure 1 illustrates the change in cargo distribution before and after the establishment of inland ports. The establishment of inland ports improves port collection and distribution systems, reduces regional logistics costs, and promotes local economic development. Furthermore, Figure 2 presents the operational mechanism of container intermodal scheduling, which necessitates integrated planning across multiple transport modes. The goal is to optimize the allocation of logistics resources while balancing economic and environmental objectives. The inland container intermodal scheduling problem is a complex combinatorial optimization task with multiple nonlinear objectives and operational constraints. Traditional optimization techniques, such as dynamic programming and linear programming, often fail to provide efficient solutions for large-scale and real-time problems. In contrast, metaheuristic algorithms have gained attention for their global search capabilities and flexibility. Among these, the grey wolf optimizer (GWO) and Harris hawks optimization (HHO) have shown promising results in solving complex multi-objective problems. However, GWO is prone to premature convergence due to its limited local exploitation, while HHO lacks robustness in global exploration. Optimization algorithms have demonstrated their effectiveness across various domains. In the field of the Internet of Things, both arithmetic optimization and hybrid metaheuristic algorithms have been used to enhance service composition and security. For example, improved shuffled frog leaping algorithms that integrate chaos theory, reverse learning, and Gaussian mutation have outperformed conventional methods [1,2]. In  
Figure 1: Impact of inland ports on cargo distribution in coastal ports. **(a)** Transport routes before inland port implementation; **(b)** transport routes after inland port implementation. Figure 2: Container loading scheduling process. logistics, hybrid strategies have led to significant improvements in cold-chain logistics, vehicle routing, and sustainable supply chain design. These strategies include the combination of a large neighborhood search with non-dominated sorting genetic algorithm II (NSGA-II), tabu search with GWO, and NSGA-II with multi-objective particle swarm optimization [3; 4; 5]. Furthermore, advanced variants, such as the firefly algorithm integrated with the spotted hyena optimizer and GWO enhanced with artificial bee colony operators, have improved convergence, route optimization, and fleet sizing in multi-objective scenarios [6; 7; 8].