## 1 Introduction
Applying mixed-mode linear programming (MILP) models helps one most effectively manage a heterogeneous vehicle fleet--that is, one using several vehicle kinds or capacities. The model proposed by [PERSON] et al. [7], which includes trip coordination, speed regulation and stop skipping, is a significant example of how to reduce both passenger waiting time and vehicle operating costs. Dynamically applied planning models allow for more flexible responses to passenger needs and transport infrastructure capabilities. Large industrial sites, where staff transportation flows may be erratic and dependent on work shift schedules, find this method very appropriate. Second, the modernization of transportation networks depends much on autonomous and semi-autonomous technologies. [PERSON] et al. [8] propose a semi-autonomous (SAE level 4) transport system that allows for the formation of bus platoon services, adjusting dynamic capacity and autonomous driving in certain geographical areas. The advantage of this model is the ability to reduce passenger waiting times and transport operating costs by avoiding empty buses. The results of the study showed that such technologies can significantly improve service efficiency and ensure vehicle utilization. For instance, the availability and future aims of autonomous vehicles (car, bus, truck, etc.) could be a good example of this issue. According to [PERSON] et al. [9]'s study, higher percentage of autonomous vehicle networks (MPR) can greatly lower traffic congestion and pollution, as well as raising the average speed of the network. This model underlines that improving both transport efficiency and environmental sustainability depends on well-tuned transportation speeds and planning decisions. Third, being a particular kind of transportation service, employee transportation services call for certain kind of planning. [PERSON] et al. [10]'s study on urban air mobility (UAM) highlights the importance of route optimization and vehicle capacity in order to reduce passenger travel times and maintain service availability. Although UAM solutions are currently more adapted to air transport, their methodological principles could also be applied in the context of land transport, especially in complex work environments, such as shipyards. Fourth, mobility as a service (MaaS) is another promising direction for optimizing employee transportation. [PERSON] and [PERSON] [11] emphasize that the quality and accessibility of transport services directly influence passenger choices. In this context, not only network development is important but also research into the behavior of real users, which would allow the alignment of technological solutions with practical needs. Furthermore, [PERSON] [12]'s study on the sharing economy shows that automobile sharing systems might help lower transportation costs and environmental pollution. Finally, one should take into account the social and legal consequences of implementing fresh mobility technologies. As [PERSON] et al. [13] underline, the effective integration of autonomous vehicles depends on the collaboration of local authorities, precise legal rules and social acceptance. Moreover, the policy and regulatory environment surrounding urban mobility must evolve to accommodate emerging transport innovations. Implementing intelligent fleet management solutions requires collaboration between government authorities, industry leaders and research institutions. Social acceptance of mobility shifts, safety concerns regarding autonomous shuttles and legal frameworks governing shared transport systems are crucial aspects that influence adoption and scalability [13]. The study focuses on optimizing employee shuttle services using dynamic linear programming (DLP) to address cost inefficiencies and improve sustainability at the Yalova Shipyard. By applying a structured optimization model, this research aims to  
* Reduce fleet operational costs by optimizing the number and type of service vehicles.