## 2 State of the Art
### Simulation Approaches in Urban Logistics  
Several simulation approaches exist in the literature [27]; among them, three main categories can be identified. There are three primary approaches to simulations used in this domain: discrete event simulation, agent-based simulation, and SD. Discrete event simulation models the logistics system as a series of discrete events that occur over time. Each event represents a change in the system's state, such as the arrival of a vehicle, the loading or unloading of goods, or the completion of a delivery. This type of simulation is particularly useful for analyzing complex systems where events occur non-continuously and for studying the interactions between different logistics components, such as warehouses [29], unloading infrastructure [26], vehicles, and distribution centers, also integrating material elements equipped with sensors and immaterial elements, as in the case of digital twins [31]. It helps in identifying bottlenecks, optimizing resource allocation, and improving process efficiency. This optimization and efficiency improvement directly supports the Industry 5.0 pillar of resilience, enabling urban logistics systems to better withstand disruptions and adapt to changing conditions. Agent-based simulation models the system through the interaction of autonomous agents, each with its own behaviors and objectives. Agents can represent various elements of the logistics chain, such as vehicles, operators, customers, or even infrastructure. This approach is useful for studying emergent behavior resulting from the interaction of multiple agents and for evaluating the impact of logistics management policies on both local and global scales. For example, it can be used to simulate the behavior of truck drivers in response to different traffic policies, toll charges or decision processes of the different nodes in a supply chain [32]. SD is a methodology used to study and manage complex interactions characterized by feedback loops and time delays, commonly found in business and social contexts. It relies on computer simulation to explore how system structure influences behavior over time [33; 34]. In the context of logistics, SD simulation models the dynamic interactions among system components using stock-and-flow diagrams, helping to reveal how feedback mechanisms and delays shape overall system behavior. This type of simulation is particularly valuable for strategic planning and policy analysis, as it helps in identifying leverage points and predicting the long-term impact of different logistics strategies, but also for companies in order to improve activities, like as supply chain management [35]. SD is ideal for examining how changes in one part of the system can ripple through and affect the whole, making it a powerful tool for addressing systemic issues in logistics. It is especially useful for understanding the broader implications of policy decisions and for developing sustainable logistics practices. The long-term impact assessments and development of sustainable logistics practices through SD directly contribute to the sustainability pillar of Industry 5.0, promoting environmentally conscious urban freight management. Studying transportation and urban logistics with an SD approach is crucial for a comprehensive understanding of complex urban logistics systems. It allows researchers to model various factors like vehicle fleets, traffic flow, and access management, revealing dynamic system behaviors over time. Moreover, this approach enables long-term impact assessments of policy interventions and logistical strategies, aiding decision-makers in formulating effective urban logistics policies. Additionally, these models help identify synergies and trade-offs between policy objectives, enabling policymakers to make informed decisions that balance competing priorities.