## 6 Conclusions and Suggestions  
This study demonstrates the potential of optimizing employee shuttle services through dynamic linear programming (DLP) to enhance transportation efficiency, reduce operational costs and support sustainability. The findings highlight that optimizing fleet composition and scheduling at the Yalova Shipyard significantly reduces fuel consumption (33.9%), maintenance costs (35.2%) and annual tax expenses (49.3%). By removing excess vehicles and improving fleet utilization, the proposed model offers a scalable and adaptable approach to managing large-scale employee transportation networks. Beyond the immediate financial benefits, the research underscores the broader impact on the regional transportation ecosystem. Reducing the number of vehicles on the road contributes to lower traffic congestion, leading to improved commute times and decreased stress for employees. Additionally, a more efficient shuttle service minimizes the reliance on private vehicles, which in turn reduces parking demand and urban sprawl related to transport infrastructure. By lowering fuel consumption and CO2 emissions, the study aligns with global sustainability goals, enhancing air quality and mitigating environmental degradation. Furthermore, the model presented in the study has strong potential applicability beyond the shipyard context. It can be adapted effectively to various industries facing similar transportation challenges, such as manufacturing plants, corporate campuses, logistics centers and other large-scale employment hubs. The flexibility and scalability of the dynamic linear programming (DLP) method enable it to accommodate diverse fleet compositions, scheduling complexities and dynamic demand fluctuations, making it suitable for broader applications in urban planning and smart transportation systems. Nevertheless, the implications of this research extend beyond employee mobility. In the field of transportation planning, the study contributes to a growing body of work that advocates for data-driven optimization to improve operational efficiency and reduce environmental impact. Municipal governments and urban planners could adapt similar models to enhance public transit systems, reduce congestion and promote sustainable transport alternatives. Furthermore, the research holds implications for corporate mobility management, offering insights into how businesses can improve employee transportation while simultaneously reducing costs and carbon footprint. Industries with large workforce mobility requirements, such as manufacturing, logistics and construction, could leverage similar approaches to optimize fleet utilization and create cost-effective transport solutions. From a policy perspective, the proposed methodology could further benefit industries that aim to integrate sustainable practices, including the adoption of electric or autonomous vehicle fleets, thereby supporting broader sustainability and environmental objectives. It may provide empirical evidence supporting incentives for shared transportation, smart mobility integration and fleet electrification. Policymakers could use these findings to design urban mobility strategies that prioritize efficiency and sustainability, potentially influencing tax incentives, emissions regulations and transport funding allocations. Additionally, this research intersects with sustainability studies, as it highlights how optimizing transport services contributes to reducing the environmental impact. For future work, expanding the dataset and incorporating predictive analytics could further refine demand forecasting, ensuring even greater efficiency. The integration of autonomous and electric vehicle fleets presents another promising direction, aligning employee transportation with emerging green mobility trends. Additionally, exploring mobility as a service (MaaS) integration with other public and private transport options could enhance accessibility and optimize regional transport networks.