Complexity Methods for Predictive Synchromodality
Synchromodality is a highly powerful and promising concept for boosting the efficiency of freight transportation, based on combining multiple transportation modes (barges, trucks, trains) in a smart way. This makes a transition possible from the delivery of plain logistic services to integrated services by exploiting the complementary nature of available transportation modes.
The problem is that under the current state of the art, the exploitation of synchromodality is strongly challenged by the inherent complexity of logistic supply chains due to the omnipresence of uncertainty (weather, delays, transport demand, disruptions, traffic dynamics, driver behavior), influencing many decisions of many stakeholders. Motivated by this, we propose in this project the concept of predictive synchromodality, incorporating models, methods and tools based on predictive data analysis and stochastic decision making in distributed control environments, for exploiting the great potential of synchromodality, addressing the question what to transport, how and when. This is the only way in which the gap from the intransparent and inefficient current transport state can be bridged to a streamlined logistic system with improved transport efficiency, higher loading rate of vehicles, less emissions and costs.
To secure the alignment of the research to the real needs from the logistics sector, we investigate three real-life use cases identified with our industry partners as an integral part of the project: predictive synchromodality for container logistics, dry bulk logistics, and air cargo logistics.