Data-Driven Multiobjective Multimodal Transportation Route Optimization in Hybrid Uncertain Environments

Multimodal freight transportation is essential for enhancing logistics efficiency and reducing costs, with route optimization as its core component. However, uncertainties are prevalent in multimodal systems, posing challenges for the construction and validation of simulation models. Furthermore, the involvement of multiple stakeholders introduces conflicting optimization objectives. Effectively balancing these objectives to maximize overall benefits has become a critical issue that requires resolution. For the multimodal transport route optimization problem in hybrid uncertainty environments (e.g., there are uncertainties in transportation demand, transportation time, and transfer time), this study constructs a multiobjective optimization model based on fuzzy numbers. The complexity of the model is reduced by introducing the chance-constrained programming theory. To solve the model, a data-driven multi-objective evolutionary algorithm is designed, integrating Monte Carlo simulation with surrogate models to effectively reduce the computational cost of uncertainty estimation. Furthermore, a constraint prioritization strategy is developed to handle multiple conflict objectives, and complex constraints efficiently. Simulation results demonstrate that the proposed algorithm exhibits excellent performance across networks of varying scales, providing robust decision support for multimodal transportation decision-making.