Thesis defense of Arturo Pérez Rivera, June 29, 2018

On Friday, June 29, 2018, Arturo Pérez Rivera will defend his PhD thesis “Anticipatory Freight Scheduling in Synchromodal Transport”.
This thesis has been supervised by prof.dr. J. van Hillegersberg and M.R.K. Mes.
The ceremony will take place in the Prof.dr. G. Berkhoff-zaal of the Waaier at University of Twente, at 14:30 hrs.

We study anticipatory scheduling decisions for the transport of freight in a synchromodal network. In a synchromodal network, the choice of mode, the choice of transport path, and the timing of operations is not fixed up front, but decided upon various moments using the latest information about the status of the transport modes and about freight demand. This increased flexibility brings opportunities for consolidation and options for efficient transport, throughout the network and throughout time. However, to achieve such gains, transport decisions must consider their consequences in the entire network and anticipate on their effect in future decisions. We use four different perspectives for studying the decisions in a synchromodal network, based on traditional scheduling methods for multi-modal transport considered in the literature. For each perspective, we develop mathematical models and heuristic algorithms that support anticipatory scheduling decisions in synchromodal transport. Furthermore, for each perspective, we evaluate the output of our models and algorithms using simulation-based experiments, and provide insights into their efficiency gains over traditional scheduling methods, using different network characteristics. As an addition to the development of scheduling methods, we study how to increase the awareness about the trade-offs considered by our methods and how to facilitate the adoption of our algorithms through a serious game. Finally, we present a closing reflection about our anticipatory scheduling methods for freights in synchromodal transport, and provide an outlook for further research with respect to extensions of the models, improvements of the heuristics, and implementation aspects.