## 2 Background and Related Works  
Container intermodal transport plays a pivotal role in global logistics, significantly contributing to low-carbon development. Although it represents only 16% of global maritime cargo by weight, it accounts for nearly 60% of the value of international trade, highlighting its importance in global supply chains [9]. Shifts to more sustainable transport modes, such as rail and waterborne transport, have demonstrated measurable environmental benefits. For instance, the expansion of these modes, alongside the adoption of electric trucks at Shenzhen Yantian Port, has led to a reduction in carbon emissions [10]. The International Energy Agency's 2023 report on CO\({}_{2}\) emissions identifies the transport sector as the largest contributor to the global rise in carbon emissions, with an increase of 240 million tonnes, predominantly driven by road transport. Road transport accounts for 30% of global freight and 5% of energy-related carbon emissions [11; 12]. In contrast, inland waterway transport exhibits superior energy efficiency, requiring only 17% of the energy used by road transport to carry the same freight volume, and consuming approximately half the energy of rail transport [13]. These trends highlight the potential of container intermodal transport to enhance energy efficiency, reduce emissions, and support the sustainable transformation of logistics systems. Path optimization for container intermodal transport has been extensively studied, with early models primarily focusing on transport cost, cargo characteristics, and infrastructure constraints. Recent multimodal routing approaches increasingly incorporate both economic and environmental objectives to support sustainable logistics. To improve network efficiency and reduce operational losses, advanced models have introduced capacity constraints, multi-commodity flow formulations, and cold-chain logistics considerations [14; 15; 16]. Although transport cost remains the primary factor in decision-making, several extended models have incorporated elements such as vehicle capacity, spoilage rates, and time-based penalties to better reflect practical logistics conditions [17; 18]. However, most existing models address these factors separately, lacking a unified framework that simultaneously integrates economic, operational, and environmental objectives. This limitation reduces their applicability to real-world logistics systems, where multiple inter-dependent constraints must be considered holistically. Various optimization approaches have been explored for container intermodal transport, including heuristics, hybrid models, and simulation-based strategies. Heuristic techniques, such as approximate dynamic programming, have improved scheduling efficiency under stochastic demand conditions [19]. Hybrid frameworks combining robust and possibilistic programming have been developed to address sustainability goals while coping with parameter uncertainty [20; 21]. Simulation models have been applied to represent complex operational dynamics, including underground container handling, thereby offering decision support under realistic operational constraints [22]. Moreover, the integration of particle swarm optimization with dynamic programming has enhanced network recovery strategies and improved system resilience against disruptions [23; 24]. Despite these contributions, limited research has examined adaptive hybrid strategies that effectively integrate global and local search capabilities, which are critical for achieving high convergence accuracy and robustness in large-scale multi-objective optimization problems. Time window constraints are fundamental in intermodal scheduling, with soft time windows offering greater flexibility than rigid constraints. Several studies have developed multi-objective models that incorporate elastic or mixed time windows to simultaneously optimize transport cost, travel time, and emissions [25; 26].