Enhancing Inter-Terminal Transport via Early Information

We focus on decoupling decisions of inter-terminal transport (ITT) vehicles at a logistics node, such as a port or business park. We assume the node faces a stochastic flow of trucks delivering and retrieving containers from logistics companies (LCs), i.e., warehouses and terminals. During peak hours, trucks may stop at a parking area, where the ITT fleet, composed of electric and automated vehicles (EAVs), takes over the transport of containers between the parking and the LCs. The decoupling decision (DD) determines whether trucks should proceed to their LC or park. The decision model is based on few parameters, such as the estimated workload of the ITT fleet over a time window. We integrate the decision model into a discrete event simulation of the Port of Moerdijk, the Netherlands, allowing experimentation with various arrival patterns, earliness of information, and DD parameters. The simulation involves a realistic traffic simulation, more than 130 LCs, and up to 100 EAVs. Through parameter calibration, the decision model capitalizes on early information to improve service levels and reduce kilometers driven by conventional trucks.