## 3 Materials and Methods
### Agent-Based Simulation Using Anylogic  
AnyLogic is a versatile simulation platform that supports hybrid approaches including DES, System Dynamics (SD), and ABS [24], making it suitable for modeling complex systemssuch as logistics networks. Its robust ABS capabilities enable detailed simulation of interactions between diverse agents such as vehicles, facilities, and storage points, programming agents with specific behaviors and rules [25]. This adaptability is further enhanced by its ability to integrate real-time data, ensuring that models remain responsive to changing conditions and provide actionable insights. Designed for large-scale applications, AnyLogic efficiently manages high volumes of data and interactions, making it ideal for dealing with complexities like PDP [26]. Furthermore, its advanced visualization tools provide dynamic insights into system behavior, aiding optimization, while its seamless integration with systems such as GIS, Manufacturing Execution Systems (MES), and Building Information Modeling (BIM) ensures realistic and comprehensive simulations [25]. These capabilities collectively position AnyLogic as a relevant tool for analyzing and improving logistics operations. The applicability of AnyLogic in the context of urban logistics is exemplified in the flowchart presented in Figure 1, which illustrates an integrated system for order management, pickup, storage, and delivery. This model reflects the complexity of the PDP by incorporating multiple agents, such as vehicles, order operators, and warehouses, all working in synchrony to optimize the flow of goods. AnyLogic's ability to simulate interactions between these agents, combined with real-time data integration and support for advanced visualizations, is essential for capturing the dynamic details of this process. Based on the conceptual model previously presented in Section 3.1, the next subsection will detail the simulation implementation, describing how these functionalities were employed to create a virtual environment that enables the analysis and realistic representation of the company under study. The implementation of the ABS model using AnyLogic 8.7.9 Personal Learning Edition requires the execution of essential methodological steps to ensure the representativeness of the company studied logistical process. This procedure aims to provide a solid foundation for obtaining quantitative and qualitative insights focused on operational improvement. Table 2 presents a summary of the main attributes and behaviors modeled for each agent in the system. As outlined in Table 2, the main agents include orders/task, vehicles, and warehouses, each with specific attributes that influence operational decisions. Tasks are defined by factors such as geographic location, pickup and delivery time, and luggage volume, while vehicles have capacity and mobility constraints. Warehouses, when utilized, serve as temporary storage points, directly impacting the distribution process. The interaction between these \begin{table}
\begin{tabular}{c c c} \hline \hline
**Agent** & **Attribute** & **Description** \\ \hline \multirow{6}{*}{**Order/Task**} & Date & Date for Pickup and delivery \\ \cline{2-3}  & Time & Time for collection and delivery \\ \cline{2-3}  & Address & Location of collection and delivery \\  & & (latitude and longitude) \\ \cline{2-3}  & Dimension & Dimensions of the merchandise \\ \hline \multirow{6}{*}{**Vehicles/Driver**} & Capacity & Volume/weight of the merchandise \\ \cline{2-3}  & Load Capacity & Vehicle carrying capacity \\ \cline{2-3}  & Address Time & Time to reach the location \\ \hline \multirow{2}{*}{**Warehouse**} & Storage Cost & Cost associated with storage \\ \cline{2-3}  & Storage Capacity & Maximum supported volume \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Agents used in the simulation model. agents shapes the system's behavior, where efficient driver allocation and storage management play a crucial role in optimizing routes and reducing operational costs.