## 6 Conclusions  
This paper investigated various methods for optimizing elevator-car dispatching to minimize passenger waiting time. We established a finite-state machine representation of the elevator group control system to understand its dynamics and analyzed two primary heuristics: directional continuity and final call. While these heuristics offer some improvements, they exhibit limitations. To overcome these limitations, we explored two optimization methodologies: the genetic algorithm (GA) and simulated annealing (SA). We designed solution representations and fitness evaluations for both approaches and employed a simulation-based optimization approach to identify their optimal parameter values. A comparative analysis revealed that the GA-based approach significantly outperformed both existing heuristics and the SA-based method. This suggests that GA holds significant promise for optimizing elevator group control and minimizing passenger waiting time in real-world scenarios. Overall, this research contributes valuable insights and practical solutions to the field of elevator group control. By leveraging GA-based optimization, we can significantly reduce passenger waiting times and enhance the overall efficiency and user experience of elevator systems. However, genetic algorithms are characterized by their iterative nature and can take a long time to calculate, especially in the case of large populations and many iterations. The selection of parameters becomes crucial, emphasizing not only the accuracy of the algorithm but also the travel time available for the elevator between events (calls). However, the proposed genetic algorithms in this context show the ability to perform well in a very short time due to their efficient fitness assessment process. This allows rapid evaluation and optimization despite the possible complexity of the problem area, ensuring practical feasibility within the operating constraints of the elevator system. Future research could explore further enhancements to the GA-based approach, including the incorporation of dynamic passenger-arrival information and real-time traffic conditions. Additionally, investigating the application of other optimization algorithms \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Floor**} & \multicolumn{8}{c}{**AWT**} \\ \cline{2-13}  & \multicolumn{3}{c}{**2 Cars**} & \multicolumn{3}{c}{**3 Cars**} & \multicolumn{3}{c}{**4 Cars**} & \multicolumn{3}{c}{**6 Cars**} \\ \cline{2-13}  & **HR1** & **HR2** & **GA** & **HR1** & **HR2** & **GA** & **HR1** & **HR2** & **GA** & **HR1** & **HR2** & **GA** \\ \hline
10 & 70.2 & 65 & 58.3 & 59.9 & 53.7 & 44.1 & 49.2 & 46.5 & 38.9 & 37.6 & 34.3 & 29.1 \\
11 & 73.6 & 67.3 & 60.4 & 64.2 & 62.5 & 51.8 & 57.3 & 52.8 & 45.4 & 48 & 44.7 & 39.6 \\
12 & 75.1 & 69.7 & 64.1 & 71.6 & 65.3 & 56.4 & 66.2 & 61.9 & 50.8 & 59.2 & 55.3 & 46.2 \\
13 & 77.8 & 71.5 & 66.7 & 73.7 & 69.6 & 59.3 & 70 & 64.7 & 54.2 & 64.5 & 59.2 & 51 \\
14 & 80 & 74.8 & 68.5 & 76.5 & 71.4 & 63.7 & 72.6 & 68.1 & 59 & 67.4 & 63.4 & 53.9 \\
15 & 84.2 & 76.9 & 70.2 & 80.3 & 73.2 & 65.9 & 75.1 & 71 & 63.7 & 70.9 & 67.1 & 57.8 \\
16 & 87.7 & 78.4 & 73.1 & 84.9 & 78.9 & 68.2 & 79.8 & 74.6 & 66.1 & 75.2 & 70.9 & 61.4 \\
17 & 90.4 & 81.6 & 75.8 & 88.1 & 83.5 & 72.4 & 81.3 & 77.2 & 68.6 & 78.8 & 74.2 & 64.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: AWT results for GA and heuristics algorithms in a 5-min period. and hybrid approaches combining GA with other techniques could lead to further improvements in elevator dispatching efficiency. Furthermore, in forthcoming research endeavors, our objective is to conduct a comparative analysis among genetic algorithms (GAs), moth-flame optimization (MFO), the sparrow search algorithm (SSA), differential evolution (DE), and particle swarm optimization (PSO). This comparison aims to explore the subtle performance variations of these metaheuristic optimization algorithms.