针对煤炭联运过程中集装箱大规模到达时，会造成节点换装过程中集装箱挤压和中转延误等问题。基于此，采用量化管理理论中的“人、机、料、法、环”五维框架对节点换装过程中产生的中转延误风险进行评价，以客观赋权法（criteria importance though intercrieria correlation, CRITIC）对煤炭公铁联运中转延误风险进行量化评估，进而将风险纳入路径优化的考虑因素中来，同时引入改进的活动总量-模式分配-能源强度-排放因子（activity-share-intensity-factor, ASIF）方程对运输和中转节点换装过程的碳排放进行度量，建立全货运时间内的碳排放和中转延误风险模型。基于上述模型，提出以最低碳排放、最小运输成本、最短运输时间、最低中转延误风险为目标的煤炭公铁联运路径优化模型。以运输实例分析的过程，引入了MATLAB编程，设计非支配排序遗传算法Ⅱ（non-dominated sorting genetic algorithm Ⅱ，NSGA-Ⅱ）算法对算例进行求解，分别模拟了不同运输决策条件下的各优化结果，以最低碳排放、最小运输成本、最短运输时间、最低中转延误风险的单目标优化结果，与综合考虑上述目标的多目标优化结果对比，探究多目标优化在处理煤炭公铁联运低碳路径优化上的优势。研究结果表明：多目标优化方案相比单目标优化能够有效地降低运输费用，缩短运输时间，减少综合能耗，并降低中转延误风险，实现煤炭公铁联运经济与安全水平的综合最优；同时，也为不同运输需求及情景下的公铁联运路径优化提供了一定的参考依据。

When a large number of containers arrive at a node during coal intermodal transportation, it can lead to problems such as container congestion and transfer delays during the transshipment process. Based on this, the criteria importance through intercrieria correlation (CRITIC) objective weighting method was adopted to quantify the risk of transfer delay in coal rail-road intermodal transportation, and then the risk was incorporated into the path optimization factors. Meanwhile, the improved activity-share-intensity-factor(ASIF) equation was introduced to measure the carbon emissions of transportation and transshipment node exchange processes. A carbon emission and transfer delay risk model for the entire freight transportation time was established. Based on the above model, a coal rail-road intermodal transportation path optimization model was proposed, with the objectives of minimizing carbon emissions, transportation costs, transportation time, and transfer delay risk. Through a case study, MATLAB programming was introduced, and the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) algorithm was designed to solve the example. The optimization results under different transportation decision conditions were simulated, respectively. By comparing the single-objective optimization results of minimum carbon emissions, minimum transportation costs, shortest transportation time, and minimum transfer delay risk with the multi-objective optimization results that comprehensively consider the above objectives, the advantages of multi-objective optimization in handling low-carbon path optimization for coal rail-road intermodal transportation were explored. The research results show that compared with single-objective optimization, the multi-objective optimization scheme can effectively reduce transportation costs, shorten transportation time, reduce comprehensive energy consumption, and lower the risk of transfer delay, achieving a comprehensive optimal combination of economy and safety in coal rail-road intermodal transportation. At the same time, it also provides a certain reference for the path optimization of rail-road intermodal transportation under different transportation demands and scenarios.