## 5 Model Results and Discussion  
Examining the results of the proposed model, the average occupancy rate of the company, which currently has thirty-four vehicles, is obtained as shown in Figure 8. It can be seen that this rate increases up to 99% after the optimization carried out with the model proposed in the study.  
The maximum number of vehicles to be accommodated in the company was determined by taking into account the maximum number of vehicles needed in the east and west directions in the same period of time--in other words, the maximum number of vehicles operating simultaneously. When analyzing Figure 9, this value for the 17 + 1 capacity vehicle gives the maximum number of \(17+1\) capacity vehicles that should be accommodated within the facility as four westbound vehicles and one eastbound vehicle on Wednesday evening when the maximum demand is at its peak. The maximum number of \(27+1\) capacity vehicles required is determined as a total of twenty vehicles, nine westbound on Tuesday morning and eleven eastbound at the same time. In this case, nine of the thirty-four vehicles allocated to the service network are wasted. This represents a gain of approximately 27% for this case study. Based on the results of the analysis, the value of the objective function as a result of the optimization was found to be USD 1695. This shows that the existing service achieves a 33.9% reduction in fuel costs (Figure 10).  
In addition to fuel costs, there are other costs that vary depending on the number of vehicles. One of these is the annual maintenance cost of the vehicles, and another is the annual tax expense. As a result of the optimization, the reduction in the number of vehicles required for the service leads to a reduction in these cost items. According to the results of the analyses, the annual maintenance cost of the vehicles is reduced by 35.2%, while the annual tax cost of the vehicles is reduced by 49.3%. Taking all these positive improvements into account, it is clear that the optimization study leads to a significant reduction in service costs.  
Figure 8: Number of vehicles required for the service network.  
It is also clearly seen from the model results that the proposed DLP model significantly enhanced the efficiency and cost effectiveness of the shuttle service at the Yalova Shipyard. The study revealed that the existing shuttle fleet was substantially underutilized, with vehicle occupancy rates averaging between 60 and 70% and sometimes falling below 50%. After implementing the DLP optimization, the occupancy rates improved dramatically, reaching up to almost maximum, demonstrating optimal utilization of the available shuttle capacities. This substantial improvement highlights the DLP model's effectiveness in aligning shuttle service offerings closely with actual employee transportation needs, thereby eliminating unnecessary costs and ensuring resource-efficient operations. Figures 9 and 10 emphasize the significant economic value provided by the optimized model through the reduction in excess vehicle use. In addition to the economic benefits, the optimized shuttle service contributes to sustainability goals by reducing traffic congestion and lowering carbon emissions, thereby promoting environmental responsibility. Overall, these findings confirm that DLP provides a robust, scalable solution that could be replicated across various industries facing similar logistical and transportation challenges.  
Figure 10: Cost analysis for the suggested system.  
Figure 9: Comparison of vehicle occupancy rate after optimization.