## 3 Results and Discussion
### Discussion
Building on this, future research could benefit from adopting a multi-objective optimization approach that reflects the complex trade-offs encountered in realistic parking scenarios. Beyond maximizing vehicle count, relevant objectives may include minimizing entrance and exit conflicts, reducing computation time, aligning vehicle orientation with slope or signage, minimizing the distance to zone entry points, and avoiding overlaps in maneuvering paths. These competing goals may be handled using various optimization strategies, such as scalarization techniques with weighted preferences [54], indicator-based evolutionary algorithms that prioritize solution quality metrics, or decomposition-based frameworks like the MOEA/D (the Multi-Objective Evolutionary Algorithm Based on Decomposition) [55]. Incorporating these methods could significantly improve the flexibility, robustness, and user-centered applicability of the proposed system. For future studies, implementing the proposed framework in real-world applications requires segmenting vehicles within the parking area and transmitting their geometric data to the optimization algorithm via a server, as in the study by [PERSON], [PERSON], and [PERSON] [31]. In real-world scenarios, when a vehicle exits or a new vehicle enters the parking area, it is crucial to rapidly determine new optimal placements. When analyzing the solution times of the MDE algorithm, the results indicate that in Scenario 1 (parallel parking), the placement of 10 to 13 vehicles required between 3.65 and 19.21 s. In Scenario 2 (limited angular freedom), the optimization process was completed within 3 to 13.05 s, while in Scenario 3 (full angular freedom), the computation time ranged from 1.94 to 9.65 s. Notably, the maximum computation times in Scenario 2 (13.05 sec) and Scenario 3 (9.65 sec) suggest that these methods could be feasible for real-time applications. These durations reflect full-capacity optimization, representing the time required to place between 10 and 13 vehicles. In typical real-time scenarios involving smaller changes, such as the arrival or departure of 1 to 5 vehicles, the computation time may be significantly shorter. In practical cases with fewer available parking spaces, the solution space would be smaller, leading to even faster computation times. Additionally, by narrowing the solution space, such as limiting rotation angles or adjusting algorithm parameters, the problem can be simplified for less complex settings, further accelerating the process. However, further reductions in computation time would be beneficial for future implementations. Since the literature includes numerous optimization methods, it is recommended that alternative approaches be investigated for this purpose. As illustrated in Figure 5, even though vehicles were optimally placed according to predefined constraints, additional restrictions may be required to enhance entry and exit convenience. In this regard, future studies should focus on both reducing optimization time and improving vehicle maneuverability, potentially through advanced constraint handling and additional optimization strategies. Future studies may also consider incorporating additional objectives such as reducing the carbon footprint, shortening parking duration, and facilitating easier entry and exit.