## 5 Results and Discussion
### Scenario 2: Optimal Scenario
For diesel-powered vehicles, an average fuel consumption of 0.041 L/km, in combination with an emission factor of 3.1 kg CO\({}_{2}\) per liter of diesel, as reported by [PERSON] [28], who evaluated the environmental impact of urban transportation across different driving modes and fuel types. By multiplying the consumption rate by the emission factor, a composite value of 0.1271 kg CO\({}_{2}\) per kilometer was obtained. Figure 5 presents the main performance indicators analyzed across the three operational scenarios: the company's real-world execution, the optimal routing derived from the optimization model, and the corresponding scenario simulated using the ABS model. Each individual order is associated with a specific travel distance, estimated CO\({}_{2}\) emissions, and travel time. These metrics are displayed per task, enabling a detailed comparison of operational efficiency and environmental impact among the three approaches. Figure 5: Comparative analysis of distancec, CO\({}_{2}\) emissions, and travel time per task across operational scenarios. Figure 5 presents a detailed comparison of KPIs across the three operational scenarios analyzed: the company's real-world operations, the optimal solution generated by the optimization model, and the simulation of this optimized solution using the ABS model. The evaluated KPIs include distance traveled, CO2 emissions, travel time (pickup and delivery), vehicle capacity utilization, number of vehicles used, and time window adherence. In terms of distance traveled (Figure 5a), both the optimization and ABS models significantly reduced the total distance compared to the company's static routing strategy. This reduction directly impacts CO2 emissions (Figure 5b), which serve as a critical indicator of environmental performance. The company's operations resulted in substantially higher emissions (29.64 kg), while the optimized scenario and its ABS yielded lower emissions--18.09 kg and 18.61 kg, respectively. These results reinforce the environmental benefits of efficient route planning and dynamic task assignment. The analysis of travel time per task (Figure 5c,d) reveals consistency between the optimization and ABS models, further validating the simulation's ability to replicate optimized outcomes. Additionally, the company required three drivers to complete the deliveries, while the optimized scenario used two, indicating improved vehicle allocation and suggesting better resource utilization. Regarding capacity utilization, none of the deliveries exceeded the vehicle's limit of 24 volume units, although several tasks operated at full capacity, highlighting effective load management under optimized conditions. Finally, time window adherence was respected in all three scenarios. However, while the optimization model had one delivery completed at the exact limit of the 10 min tolerance, the ABS had five deliveries under similar conditions, signaling proximity to the threshold but still within acceptable operational performance. A key finding is that, unlike the current scenario (Scenario 1), where three drivers were used, the optimized scenario (both in the optimization model and ABS model) required a maximum of only two drivers, demonstrating that a third driver was unnecessary for the given set of orders. The results suggest that the heuristic procedure currently employed by the company for driver assignment may be suboptimal, even under limited task volumes. This suboptimality, if not addressed, has the potential to scale disproportionately with increasing operational complexity, leading to elevated costs and reduced efficiency. Consequently, a restructuring of the company's driver allocation strategy is warranted. This could be achieved either through the implementation of mathematical optimization models or the adoption of more effective heuristic approaches that are rigorously evaluated using ABS. Such integration is expected to enhance the utilization of available resources and improve the overall cost-effectiveness of urban logistics operations.