Port-IO: a Mobile Cloud Platform Supporting Context-Aware Inter-terminal Truck Routing

The role of ports goes far beyond the traditional function of a pure transport node linking sea and landside transportation. Rather, many ports offer value-added logistics and auxiliary services leading to a network of container flows. Moreover, containers may need to be transferred between different areas within the port. Information exchange and optimization is necessary to efficiently coordinate actors and container movements involved in respective inter-terminal transports in order to avoid empty trips and thus high costs, emissions, and additional traffic volume. In this paper, we describe a novel optimization problem for addressing inter-terminal truck routing in ports and propose two greedy heuristics and a hybrid simulated annealing algorithm. The computational results are evaluated in terms of costs, empty trips, and utilized number of trucks and they indicate that the proposed hybrid simulated annealing algorithm is able to report feasible and improved routes within seconds. The optimization component is embedded into a cloud platform integrating truck drivers based on a mobile application. Beyond common functionalities, the proposed platform enables a flexible real-time assignment of truck drivers to container movements by considering the current position of trucks with the objective to better manage and coordinate inter-terminal transports. As such, our approach contributes not only with a valuable approach for vehicle planning in ports, but also presents an accessible, scalable, and multi-tenant system prototype of a mobile cloud platform for handling the interactions with truck drivers in practice, demonstrated with an example for the Port of Hamburg, Germany.