Resumen
Along with the rapid development of the economy and the increasing demand for transport quality, renewed attention to multimodal transportation has emerged. However, due to the unpredictable transport environment in the process of multimodal transportation and the uncertainties caused by the change of transport market demand, transport decision-makers face many difficulties in transport planning and routing, which has become an obstacle to the development of multimodal transportation. As an advanced form of multimodal transportation, synchromodal transportation has received extensive attention in recent years. Due to its flexibility and sustainability, synchromodal transportation can effectively deal with the uncertainty in multimodal transportation. Based on the problem of multimodal transportation networks with uncertainties, this paper proposed a mixed time-window-constrained path optimization model with the goal of minimizing the total transportation costs, and proposed corresponding assumptions considering three types of uncertainty. The model could be solved by a genetic algorithm using MATLAB software. Using this model, the best transportation path and the optimal scheme considering synchronization were obtained. The results of the case study showed that synchromodal transportation can adjust the transportation plan in time to respond to uncertainties, thus, effectively reducing transportation costs. This paper favorably supported the introduction of synchromodal transportation, which is of significance to the development of multimodal transportation in the future.