This thesis is part of the NWO research project: "Complexity Methods for Predictive Synchromodality" [140]. This program is aimed at innovative and multidisciplinary research to create new ideas for complex logistic systems. The complexity of the systems arises from the interaction between subsystems and the variability in the systems themselves. The research for this project has been done at TNO, in the department Cyber Security and Robustness. The mission of TNO is to connect people and knowledge to create innovations that boost the competitive strength of industry and the well-being of society in a sustainable way. Our research focuses on synchromodal planning problems. A synchromodal system is a multimodal network in which logistic service providers have the opportunity to switch mode of transport of containers based on real-time data. This means that customers need to book amodally and the information within this multimodal network is shared to all agents. The planning problems that arise due to this added flexibility are synchromodal planning problems. First, we perform an extensive literature study that describes research concerning synchromodal planning problems. We divide this research into strategic, tactical and operational planning. Research on operational planning is scarce, therefore our research focuses on this aspect. For synchromodal planning problems a framework categorising this research does not yet exist and therefore we will develop such a framework. Furthermore, we develop two different models of synchromodal systems. We focus on a synchromodal transportation system in which information is shared between all agents in the system. Here all agents are "selfish", i.e. choosing routes based on an individual optimisation objective. Information can be public, i.e. known to everybody in the network or private, i.e. information that belongs to a certain agent which needs to be willing to share this information. One of our models is an analytic model that relies on certain assumptions. This model is static in nature and therefore, not all aspects from real life cases are encompassed in this model. However, even with simplifications the analytic model is too computationally heavy. The other model is a simulation-based model, which can handle more realistic instances. We also develop four different methods to determine the optimal paths in such an agent-centric synchromodal network. We develop two simulation-based heuristics and two other simulation-based solution algorithms. The heuristics only act on public information, i.e. information about the congestion on the roads. The simulation-based models also act on private information, i.e. information about upcoming orders.The two heuristics and one of the simulation-based methods are then tested on a synchromodal network and on two smaller examples. The small examples show that there are instances in which the simulation-based solution algorithm outperforms the two heuristics. This means that the addition of private information can decrease costs. How...