Enhancing Electric Interterminal Transport: A Truck Decoupling System With Early Information on Arrivals

We focus on the design of a truck decoupling system employing interterminal 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 manned or automated electric vehicles, takes over container transport between the parking area and the LCs. The decoupling decision determines whether trucks should proceed to their LC, park, or decouple. The decision model is based on several parameters, such as the estimated workload of the ITT fleet over a time window. We assess the decision model using a discrete event simulation model of the Port of Moerdijk, The Netherlands. This allows experimenting with various arrival patterns, earliness of information, the charging infrastructure, and decoupling decision parameters. The simulation model involves realistic traffic behavior, six decoupling scenarios, and more than 130 LCs. Through parameter calibration, the decision model capitalizes on early information to reduce truck turnaround time and truck‐driven kilometers by up to 19%, preventing 14.4 metric tons of CO 2 eq emissions per day and reducing the share of late containers by up to 10%.