## 3 Materials and Methods  
This study proposes an ABS approach to address logistics operations and applies it to real-world data from a case company. The company focuses on the pickup and delivery of goods, primarily luggage of various sizes (i.e., small, medium, and large), in several European cities, such as Athens, Barcelona, Budapest, Lisbon, and Porto, among others. In its operational context, each service request includes a predefined collection and delivery location and must be executed within a specified time window, with a minimum of two hours' scheduling notice. Routes are dynamically generated according to demand, and loads are consolidated whenever vehicle capacity allows. In cases where the vehicle cannot complete the delivery on the same day or when it exceeds its capacity, temporary storage in a fixed warehouse is employed. While each region presents unique operational challenges, the company must meet customer-specific requirements under strict time constraints and limited resource availability. To address this issue, two methodologies are applied: (1) an adapted PDP-TW model from literature to represent the steady-state behavior of the logistics system and serving as a validation tool for the simulation model, where this model employs the Geodisc function to compute distances between points, using the standard WGS-84 ellipsoid implemented by Geopy package [21], and (2) an ABS implemented using the AnyLogic PLE platform [22]. This simulation is enhanced with geographically specific data adapted to the study area, Lisbon, Portugal, utilizing a Geographic Information System (GIS) for improved visualization. Applying these two methods is essential to assess the accuracy and applicability of each approach, enabling a more robust modeling of the logistics network. By integrating these methodologies, the study provides a comprehensive and dynamic framework for modeling, analyzing, and optimizing the company's logistics network, effectively addressing real-world variability and operational constraints. An adapted version of the optimization model proposed by [PERSON] and [PERSON] [6] is used in this study to reflect the specific operational constraints of the case company. The PDP-TW optimization model generates optimal operational scenarios based on real-world input data from the company, in which this adapted model also acts as a reference to validate the ABS model, in addition to providing inputs for the ABS model to simulate and evaluate the dynamic behavior and feasibility of these optimized solutions within the urban logistics environment. The simulation is organized into a logical sequence based on input processes, behavior, and output data. This approach reflects the dynamics of the modeled operations, ensuring an accurate representation of interactions and expected outcomes. The input data for the simulation includes the characteristics of the entities/agents involved. Each request, representing a client's pickup and delivery order, contains attributes such as luggage size (i.e., small, medium, or large), pickup and delivery locations defined by geographic coordinates (i.e., latitude and longitude), and the associated operation times. Vehicles, responsible for transportation, are described by attributes including a maximum capacity of 24 volumes, initial location in warehouse, and estimated travel time calculated based on distances between points. Additionally, the model considers optional fixed warehouses with defined locations, maximum storage capacities, and associated operational costs. The behavior of the agents is governed by rules and processes that reflect real-world operations. Orders are sorted and allocated based on baggage size and vehicle availability. When demand exceeds vehicle capacity or there are discrepancies between collection and delivery dates, orders can be directed to fixed warehouses. Alternatively, vehicles can function as mobile warehouses, respecting volume and time restrictions. Deliveries are prioritized to be completed within the timeframe established by the customer.