This paper presents a simulation framework for the logistics operations at Smart Yards. A Smart Yard is a digital and physical system enabling the collaboration of various companies at a logistics hub, e.g., seaport, airport or hinterland distribution center, and characterized by a decoupling point, automated vehicles for internal handling of cargo, and data sharing technologies. The framework is a high-level conceptual model for a hybrid Discrete Event and Agent-Based Simulation, comprising inputs, outputs, assumptions, flowcharts, and agents representing the complex interrelation of stakeholders and shared autonomous vehicles. We illustrate the concept of Smart Yard using three case studies and apply our simulation framework to one of these cases by analyzing the use of a Smart Yard at Amsterdam Airport Schiphol.

Simulation of multimodal logistics systems might require realistic modeling of the transportation networks (road, rail, air, and waterways), e.g., when evaluating the use of Automated Guided Vehicles (AGVs) on public roads or the combined use of trucks and Unmanned Aerial Vehicles (UAVs) in humanitarian logistics with disturbed infrastructure. In this paper, we propose a simulation add-on to automatically generate infrastructure networks for multimodal logistics, including logistics locations (e.g., warehouses and terminals) and various transport modes (e.g., trucks, AGVs, and UAVs) with corresponding behavior. The proposed methodology allows for various levels of accuracy and opens up possibilities for simulating physical flows of various transport modes, congestion, stochastic behavior of the network, and variable transport demand over time in a simple, quick, and accurate way. We illustrate our approach using two case studies corresponding to the examples given above with AGVs and UAVs.

Through discrete-event simulation, we evaluate the impact of using a fleet of electric and autonomous vehicles (EAVs) to decouple inbound trucks from the internal freight flows in a seaport located in the Netherlands. To support the operational control of EAVs, we use agent-based modeling and support the decision-making capabilities using a reinforcement learning (RL) approach. More specifically, to model the assignment of EAVs to container transport or battery charge, we introduce the Internal Electric Fleet Dispatching Problem (IEFDP). To solve the IEFDP, we propose an RL approach and benchmark its performance against four different assignment heuristics. Our results are compelling: the RL approach outperforms the benchmark heuristics, and the decoupling process significantly reduces congestion and waiting times for truck drivers as well as potentially improve the traffic's sustainability, against a slight increase in length of stay of containers at the port. INTRODUCTIONFreight transport volumes have been increasing for decades and are expected to more than double by 2050 (ITF 2021). This, combined with containerization, greatly affects the intermodal logistics areas that are at the heart of international freight networks, such as ports and business parks in the hinterland (Behdani et al. 2020). At these locations, a promising solution to increase throughput, and reduce congestion and operational costs is the use of automated vehicles (AVs). AV systems are a type of vehicle-based internal transport system traditionally used in manufacturing plants, distribution centers, container terminals, and other confined environments (Le-Anh and De Koster 2006). Recent advances in technology have increased the popularity of AVs along the logistics chain, where companies can now automate their logistics operations outside of private yards, for example, with AVs shunting containers between terminals in a port area.Currently, research is focused on electric automated vehicles (EAVs) due to increasing sustainability concerns and decarbonization goals (Vdovic et al. 2019). By implementing EAV systems, organizations aim to improve sustainability, flexibility, and efficiency. Furthermore, following the improvements in EAV technology and the increase in freight volumes, the authorities of logistics areas, e.g., ports and business parks, are now looking at scaling up the use of EAV systems for the whole area, thus servicing multiple logistics companies (LCs), e.g., terminals, warehouses, and cross-docking centers. The goal is to share an EAV fleet and coordinate transportation to increase operational efficiency and reduce congestion in logistics areas, thus improving both safety and throughput of goods. By sharing EAVs, the LCs can benefit from economies of scale and risk sharing, e.g., with regards to the cost of EAV ownership. The shift of paradigm