## 3 Materials and Methods
### Conceptual Model  
The simulation model serves as a structured platform to explore feasible solutions to the problem and conduct the proposed experiments. To achieve this, a simulation model was implemented, integrating the main activities associated with the approach widely recognized in the literature as the PDP, using SBA. This subsection presents the conceptual model that underpins its implementation. Figure 1 illustrates the concept of the modular simulation model, highlighting its interfaces and interconnected components. The conceptual model organizes and represents the key agents, behaviors, and interactions underlying the simulation. It provides a structured foundation for implementation, ensuring consistency with the PDP and addressing the specific operational requirements of the logistics network of the company under study. **Agents and Attributes**  
The model identifies three primary agents:  
Figure 1: Flowchart of the conceptual model to be simulated. * Order: Represents customer requests for pickup and delivery | Attributes include size (small: 1 unit, medium: 2 units, large: 4 units), geographic coordinates for pickup and delivery, and specific time windows. * Vehicles/Drivers: Responsible for transporting luggage between locations | Attributes include maximum capacity (24 units), dynamic positioning during operations, and estimated travel time based on geographic distances. * Warehouses: Fixed locations for temporary storage of luggage, used when operational constraints prevent direct delivery | Attributes include storage capacity and associated operational costs. **Rules and Behaviors**  
The simulation operates based on predefined rules:  
* Order Classification and Allocation: Orders are assigned to vehicles based on their size and the vehicle's available capacity, ensuring the total does not exceed 24 units. * Storage Decisions:  
* If the pickup date and the delivery date do not match, the goods are sent to a warehouse. * Condition 2: If the vehicle has sufficient capacity, it acts as a mobile warehouse. * Condition 3: If the vehicle does not have sufficient capacity, the goods are sent to a warehouse with the help of a vehicle designated for this situation. * Time Window Compliance: Pickup and delivery times are subject to a 10 min tolerance to account for operational delays. * Safety Assurance: Once the baggage is collected, the company under study assumes full responsibility for its safe transportation and storage. **Key Interactions**
* The simulation captures interactions between agents, such as:
* Vehicles consolidating or exchanging cargo at meeting points to optimize routes and capacity usage. * Dynamic reassignment of orders based on real-time changes in vehicle availability and capacity. **Performance Indicators**  
The conceptual model defines key metrics to evaluate and compare both the ABS and the optimization approach:  
* Capacity utilization: percentage of vehicle capacity utilized. * Time: task time and length of time luggage remains in storage facilities. * Distance travel: the distance per task and total for all vehicles. * Number of Vehicles Used: Total vehicles required to fulfill all orders. * Time Window Adherence: Percentage of pickups and deliveries completed within the allowed time tolerance. * CO\({}_{2}\) Emissions: Estimated carbon dioxide emissions based on the distance traveled and fuel consumption of diesel vehicles, used to assess the environmental impact of each scenario. Regarding integration and implementation, the conceptual model aligns with the ABS framework implemented using AnyLogic PLE (version 8.9.0). This approach leverages GIS data for accurate geographic representation and visualization, facilitating the incorporation of real-world variability into the simulation. Additionally, the optimization model serves as a benchmark for validating the ABS, ensuring the simulated dynamics adhere to established logistical standards.