Abstract
Freight transport is a critical component of global supply chains, yet it faces growing pressure from operational disruptions, sustainability demands, and land scarcity—particularly at logistics nodes such as ports and business parks. This thesis introduces the concept of the Smart Logistics Node (SLN): a next-generation logistics hub that integrates digitalization, automation, and multi-stakeholder collaboration to enhance resilience and efficiency. Central to this transformation is the adoption of Connected Automated Transport (CAT), which enables the deployment of connected automated vehicles for hub-to-hub transport within and between terminal areas.
We develop a discrete-event simulation framework that models logistics nodes and evaluates the effects of CAT adoption under a wide range of operational and technological conditions. The framework supports advanced control strategies, including proactive dispatching, electric fleet management, and container decoupling from inbound trucks at dedicated facilities, such as parking areas with driver amenities. The approach is validated through empirical simulations at four Dutch logistics nodes—an intermodal business park, a major airport, and two seaports—each facing distinct operational challenges.
Results show that CAT technologies can significantly reduce congestion, emissions, and vehicle idle times—especially when combined with early information on truck arrivals and intelligent fleet dispatch logic. Furthermore, the thesis formulates and solves the Electric Fleet Dispatching Problem using reinforcement learning, illustrating the potential of data-driven control policies in complex logistics environments.
The SLN concept, simulation framework, and findings provide actionable insights for logistics stakeholders and policymakers seeking to modernize freight operations amid increasing demand and environmental constraints. By delivering a robust simulation-based assessment of CAT technologies and decision-support systems, this research bridges the gap between innovation and real-world implementation, contributing to the sustainable transformation of logistics infrastructure.
We develop a discrete-event simulation framework that models logistics nodes and evaluates the effects of CAT adoption under a wide range of operational and technological conditions. The framework supports advanced control strategies, including proactive dispatching, electric fleet management, and container decoupling from inbound trucks at dedicated facilities, such as parking areas with driver amenities. The approach is validated through empirical simulations at four Dutch logistics nodes—an intermodal business park, a major airport, and two seaports—each facing distinct operational challenges.
Results show that CAT technologies can significantly reduce congestion, emissions, and vehicle idle times—especially when combined with early information on truck arrivals and intelligent fleet dispatch logic. Furthermore, the thesis formulates and solves the Electric Fleet Dispatching Problem using reinforcement learning, illustrating the potential of data-driven control policies in complex logistics environments.
The SLN concept, simulation framework, and findings provide actionable insights for logistics stakeholders and policymakers seeking to modernize freight operations amid increasing demand and environmental constraints. By delivering a robust simulation-based assessment of CAT technologies and decision-support systems, this research bridges the gap between innovation and real-world implementation, contributing to the sustainable transformation of logistics infrastructure.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Supervisors/Advisors |
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| Award date | 10 Jul 2025 |
| Place of Publication | Enschede |
| Publisher | |
| Print ISBNs | 978-90-365-6685-8 |
| Electronic ISBNs | 978-90-365-6686-5 |
| DOIs | |
| Publication status | Published - 10 Jul 2025 |
Keywords
- Simulation and modelling
- Reinforcement learning
- Connected automated transport
- Logistics
- Intelligent transport systems
