## 2 Literature Review
### Research Status
[PERSON] [25] combined low-carbon issues and logistics issues in the process of research long ago and analyzed the significance of logistics operation mode decision-making on carbon emission reduction through relevant mathematical models, which has a certain inspiration for similar research. Considering the randomness and uncertainty of energy consumption, [PERSON] [26] used stochastic optimization theory to establish a green multimodal transport path optimization model under mixed uncertainty with transportation costs, carbon emission costs, and time penalty costs as objectives. From the perspective of multimodal transport operators, [PERSON] [27] established a multi-task multimodal transport path comprehensive optimization model considering carbon emissions. Although the above research takes into account the impact of carbon emissions in transportation problems such as logistics operations, the research on the introduction of multimodal transport route optimization in combination with the exchange time cost of intermodal goods is still blank. The literature [10; 28; 29; 30] mainly studied the comprehensive optimization models and methods of routes and modes of transportation for multiple transportation tasks considering carbon emissions. [PERSON] [31] combined the factors of cost, time, and energy consumption to establish a multimodal transport network optimization model that meets the requirements of time, cost, and carbon emissions. Based on the research background of China-EU container intermodal transportation, [PERSON] [32] highlighted the optimization objectives of time, cost, and carbon emissions, constructed the optimization model of a China-EU container intermodal transportation path, and designed a genetic algorithm to solve it. [PERSON] [33] focused on the issue of carbon emissions. Since the gas that produces carbon emissions is mainly carbon dioxide in multimodal transport, he studied the multimodal transport network with carbon dioxide emission reduction targets and pollution emissions. [PERSON] [34] linearized the nonlinear integer programming model and analyzed the cost changes of single transport modes and multimodal transport schemes based on considering carbon emissions. On the basis of the model objectives and carbon emission constraints, it is necessary to further solve the multimodal transport path optimization problem. Therefore, in recent years, most studies have begun to focus on multi-objective optimization algorithms. The existing research has often used the weight coefficient method for multi-objective models, which expresses the relative importance of the factors with a certain weight and carries on the weighted summation to realize the transformation from multi-objective to single-objective tasks [35; 36; 37]. The research of a multimodal transport route optimization algorithm involves the Dijkstra algorithm, genetic algorithm, improved genetic algorithm, ant colony algorithm, the combination of genetic algorithm and Dijkstra algorithm, genetic algorithm based on k shortest path, and so on. [PERSON] and [PERSON] [31] established an optimization model of a multimodal transport network aiming at minimizing cost, time, and carbon emissions, designed a genetic algorithm to solve it, and analyzed an example of import and export containers in an inland city in China. They proposed the influence of coordinated transition time and different carbon emission limits on the design of their multimodal transport network. However, its essence was a dual-objective optimization model. Based on the traditional genetic algorithm, the constraints of the optimization problem could not be fully expressed, which increased the workload and solution time, and the goal of the lowest carbon emissions was not considered. Secondly, the literature [31] used the general MILP solver cplex12.4 to obtain the Pareto optimal set. It did not use some meta-heuristic algorithms needed to solve large-scale cases but used the solver to solve small-scale calculation problems. The solution method has limitations.