# Optimization of Multimodal Transport Paths Considering a Low-Carbon Economy Under Uncertain Demand
## 6 Conclusions  
Based on the perspective of a low-carbon economy, this paper constructs a robust optimization model for multimodal transport reflecting different low-carbon policies under demand uncertainty. The characteristics of different low carbon policies and uncertain demand of cargo transportation are taken into account, as well as the fact that railroads have departure time restrictions and airplanes have no-fly periods. The simulated annealing-ant colony hybrid algorithm is designed to solve the problem, and the established multimodal transportation robust path optimization model is verified by example. The comparison of the algorithms shows that the improved algorithm is feasible. Meanwhile, the following conclusions are drawn:  
1. In the process of multimodal transportation, the transportation cost and carbon emissions under different low-carbon policies will also change. With different departure times, the transportation cost will also change accordingly. This study provides new ideas for multimodal transportation path selection under different low-carbon policies, while the designed algorithm is applicable to multimodal transportation path optimization problems under uncertain demand, which can produce better solutions with better robustness. It can also be extended and applied to other shortest-path problems.
2. In future research, the dual uncertainty in transportation time and transportation demand can be considered, and the carbon emission problem can be combined with the nature of goods, such as agricultural products, dangerous goods, etc. Carbon tax (carbon price), time constraints, economic costs and other factors can be taken into account so that optimal decision-making can be carried out and the environment can be protected at the same time.  
Figure 10: Map of pathway options under mandatory carbon emissions.  
Figure 9: Solution results of the robust optimization model under different carbon emission policies.  
**Author Contributions:** Conceptualization, Z.L. and S.L.; methodology, Z.L.; software, S.Z.; validation, Z.L., S.L. and S.Z.; formal analysis, S.Z.; investigation, S.L.; resources, Z.L.; data curation, S.Z.; writing--original draft preparation, Z.L.; writing--review and editing, S.Z.; visualization, S.Z.; supervision, S.L.; project administration, Z.L.; funding acquisition, Z.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Research Program of Higher Education Institutions in Henan Province (Grant No. 23A580007). The Team Building Project for Graduate Tutors in Chongqing (Grant No. JDDSTD2022004). Supported by project of science and technology research program of Chongqing Education Commission of China. (No--KJQN202400750).  
**Data Availability Statement:** [[https://www.sintef.no/projectweb/top/contact-information/](https://www.sintef.no/projectweb/top/contact-information/)]([https://www.sintef.no/projectweb/top/contact-information/](https://www.sintef.no/projectweb/top/contact-information/)) (accessed on 26 October 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.