# Optimization of Multimodal Transport Paths Considering a Low-Carbon Economy Under Uncertain Demand
## 2 Literature Review  
This paper focuses on the review of the low-carbon multimodal transportation path optimization problem under uncertain demand, which is reviewed in three main areas: demand uncertainty, low-carbon path optimization and heuristic algorithms. Since [PERSON] and [PERSON] [5] proposed the vehicle routing problem, the main purpose is to reduce the transportation cost and distance. [PERSON] [6] investigated how to find vehicle routes and production schedules of 3D printers mounted on vehicles with a minimum travel cost. Multimodal transportation, as a newly emerging mode of transportation in recent years, and its related optimization has gradually attracted the attention of researchers. [PERSON] [7] overviewed the synchronous logistics multimodal transportation problem, and [PERSON] [8], in turn, solved the synchronous multimodal transportation supply chain problem through the SYNCHRO-NET method. [PERSON] [9] analyzed the multimodal trans-shipment problem and proposed the MILT for the multi-cycle synchronization of a single commodity. The MILP calculation method for the single-commodity multi-cycle synchronization-trans-shipment problem was proposed. [PERSON] [10] investigated the theory of relevant decision-makers in the intermodal transportation problem. When constructing the multimodal transportation model, problems such as seasonal demand and sudden replenishment exist in the actual cargo transportation process. Consequently, when developing a transportation scheme, cargo transportation is carried out under uncertainty in demand. Nowadays, scholars' studies on multimodal path optimization models under demand uncertainty are as follows: [PERSON] [11] developed a stochastic equilibrium selection model for users facing uncertain demand and extreme weather. [PERSON] [12] investigated the low-carbon path optimization problem under double uncertainty and designed a Monte Carlo sampling-based catastrophic adaptive genetic algorithm (CA-GA) solution. In addition, uncertain demand, besides affecting multimodal transport path selection, will also have an impact on carbon emissions in the transportation process, and there are different unit carbon emissions for different modes of transportation. Therefore, it is necessary to consider the uncertainty in demand when calculating carbon emissions, based on which transportation modes and routes are reasonably selected. [PERSON] et al. [13] considered the cold chain container intermodal route optimization problem in a time-varying network of carbon emissions and took the carbon emission limit as a constraint to establish a cold chain intermodal route optimization model. [PERSON] et al. [14] investigated the intermodal route optimization problem under the uncertainty in the time window and demand and explored the impact of the carbon tax value on carbon emission. [PERSON] [15] studied the green multimodal transportation path under the mixed uncertainty condition optimization problem with uncertain parameters using a fuzzy chance constraint model and a robust optimization model. Among the methods for solving the above path optimization problems, heuristic algorithms have better results in solving various NP-hard problems. Among them, the simulated annealing algorithm, ant colony algorithm, etc., have received attention from many scholars. [PERSON] [16] proposed a multi-vehicle multi-warehouse pickup and delivery problem, which improves the particle swarm algorithm in order to solve a variety of continuous and discrete problems. [PERSON] [17] developed a mixed integer model and solved it with a simulated annealing algorithm. Some scholars also used hybrid meta-heuristic algorithms to solve the problem, and more and more studies have shown that hybrid algorithms outperform single algorithms in terms of computational efficiency and results. [PERSON] [18] combined an ant colony system and simulated annealing to solve order-divisible VRP.