The goal of this thesis is to provide a method to obtain a User Equilibrium (UE) in a synchromodal transportation network in which we transport containers for multiple customers.
We use the Space Time Network (STN), in which the travel time of modalities is fixed and independent of the occupancy of the network.
We define the User Equilibrium solution as the solution wherein each customer can travel via their cheapest paths possible, and no customer is harmed by the route choice of other customers. We define the System Optimal (SO) solution as the solution in which the total costs of the network are minimized.
We expand the goal of finding a UE on the STN to finding a solution where the solution is both SO as well as UE.
The solving method we developed in this thesis, consists of finding SO solutions by solving the Minimum Cost Multi-Commodity Flow (MCMCF) problem on the STN to find an SO solution, and create tolls schemes to create a UE solution, while maintaining the SO solution.
We investigate several types of tolls in order to obtain sufficient both SO and UE solutions.

This thesis addresses synchromodal planning at operational level from the perspective of a logistics service provider. The existing infrastructure and the transportation activities are studied and modeled as an optimization problem with simultaneous vehicle routing and container-to-mode assignment. A special characteristic of this problem is the uncertain data. In other words, it is assumed that the release times of the containers belong to an uncertainty interval, and no further statistical information is available. The problem is then classified according to an extensive framework previously developed within the project. An extensive body of literature is reviewed to identify current modeling approaches and their theoretical and practical limitations. This literature study shows that, although discrete time models have been intensively investigated, there are few studies which propose continuous modeling of time. The container routing problem is modeled as a mixed integer program with explicit time variables and lateness penalties. A robust formulation is then proposed to eliminate the uncertain parameters from the objective function and constraints. By solving the new model exactly, with the aid of an optimization solver, robust solutions are obtained corresponding to transportation plans which remain feasible for any realization of the release times within the pre-specified uncertainty interval. In order to introduce some flexibility in the transportation plan, the continuous time variables are modeled as affine functions of the uncertain parameters. The resulting two-stage decision model is tested for a small-sized instance in both situations, with high and low lateness penalties. The computational results show that the adjustable robust model yields on the one hand, route-dependent adjusted solutions for the case of penalized lateness, and on the other hand, a direct improvement of the objective function for the case of tolerated lateness. The results suggest that the adjustable robust optimization framework has sufficient potential to model the synchromodal container routing problem. This thesis concludes with addressing some of the limitations of the proposed model and indicating concrete approaches for countering them.