Evaluates logistics efficiency in smart logistics nodes, highlighting improvements through automation.
The adoption of Connected Automated Transport (CAT) in Smart Logistics Nodes (SLNs) is hindered by the lack of systematic and reusable tools for quantitative ex-ante evaluation of its operational impact in complex, multi-stakeholder environments. To address this, we propose a reusable discrete-event simulation framework. The framework couples a library of modular simulation objects—a multimodal network generator, a multi-agent fleet manager, and control logic for parking, decoupling, and container dispatch—with agent-based and microscopic traffic modeling. This design enables systematic analysis of CAT’s impact on inbound flows, hub-to-hub transport, and stakeholder collaboration across diverse scenarios. We validate the framework by building detailed 3D models of four Dutch logistics nodes with open-source data and stakeholder input, and by performing a simulation study on CAT adoption for container transport. The models reproduce real infrastructure, freight itineraries, and peak-hour dynamics, confirming the framework’s fidelity and suitability for quantitative impact studies and stakeholder engagement. Results indicate 10%–40% improvements in logistics efficiency and emission reductions via connectivity, automation, and electrification of container transport. By unifying discrete event, agent-based and traffic-level perspectives, the framework supports evaluations of collaborative logistics strategies, automated and electric vehicle integration, and a broader process redesign in SLNs, thus providing a foundation for future research on logistics automation and digitization. • Simulation framework evaluates emerging technologies at logistics nodes. • Framework generates multimodal infrastructure networks using crowd-sourced data. • Agent-based control of shared, automated, and electric freight vehicles. • Framework is validated by modeling Port of Moerdijk; generates accurate freight flows. • Study shows connectivity and automation reduce dwell time, lateness and emissions.
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Brunetti et al. (2026) studied this question.
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