## 5 Review of Case Studies: Flying Vehicle Routing Optimization
These metrics offer valuable insights into the quality and efficiency of the routing solutions obtained through the GA optimization process. Figure 6 provides a comprehensive overview of the performance metrics employed in the diverse studies analyzed in this research. Notably, the majority of researchers have focused on three fundamental factors: energy consumption, carbon emissions, and routing efficiency. These metrics underscore the paramount importance of enhancing energy efficiency and sustainability while ensuring optimal routing decisions for FVs. By emphasizing these key objectives, researchers aimed to develop environmentally friendly and efficient routing solutions for FVs, which play a critical role in promoting green transportation and mitigating the environmental impact of these advanced aerial vehicles. It is essential to acknowledge that individual studies may incorporate additional metrics based on the specific requirements and complexities of their EnFV routing scenarios. Table 4 summarizes GA studies for optimizing flying vehicle routing-based applications. Figure 6: Performance metric analysis of GA-based FV routing optimization studies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Application Domain** & **Optimization Technique** & **Performance Metrics** & **Results** \\ \hline [PERSON] et al. [6] & FANETs & GA & Throughput, delay, network stability & Improved network throughput, reduced delay, enhanced network stability \\ \hline [PERSON] et al. [54] & Path planning & GA, AS-Star & Fitness improvement & GA improves A-Star solution fitness by 44.5\% \\ \hline [PERSON] et al. [55] & UAV networks & GA & Coverage, energy consumption & Improved coverage, reduced energy consumption \\ \hline [PERSON] et al. [56] & UAV task allocation & GA & Task allocation, path planning & Effective task allocation and path planning under steady wind conditions \\ \hline [PERSON] and [PERSON] [57] & UAV motion planning & GA, projective algorithms, quadratic programs & Efficient motion planning & Efficient motion planning in 2D and 3D environments \\ \hline [PERSON] et al. [58] & UAV path planning & GA, PSO & TSP optimization, path planning & More rational and effective solutions for UAV path planning \\ \hline [PERSON] et al. [59] & Quadrotor path planning & GA, RTFTN & Coverage, obstacle avoidance, energy efficiency & Effective path planning with obstacle avoidance for quadrotors \\ \hline [PERSON] et al. [60] & Delivery networks & Clustering, GA & Delivery time, energy consumption & In tandem truck-done delivery outperforms standalone systems in terms of delivery time and energy consumption \\ \hline [PERSON] and [PERSON] [22] & Inventory routing & GA & Fuel consumption, pollutant emissions & Bi-objective model to optimize fuel consumption and mitigate environmental issues \\ \hline [PERSON] and [PERSON] [12] & Capacitated VRP & NSGA-II & Travel cost, waiting time, CO\({}_{2}\) & NSGA-II algorithm provides near-optimal solutions for the 2E-CVRP \\ \hline [PERSON] et al. [19] & MDVRP & GA, FLC & Routing efficiency & GA combined with FLC improves the efficiency of the distribution system \\ \hline [PERSON] et al. [61] & Logistics distribution & GA & Delivery efficiency, route length & Joint delivery model improves delivery efficiency and reduce route length \\ \hline [PERSON] et al. [62] & Emergency logistics & GA & Distribution cost, customer satisfaction & GA minimizes overall logistics distribution cost \\ \hline [PERSON] et al. [63] & Drone delivery system & GA & Total costs & Hierarchical facility location model minimizes total costs of the system \\ \hline [PERSON] et al. [64] & UAV routing & MILP, GA, SA & Travel time optimization, UAV route scheduling & Hybrid genetic and simulated annealing algorithms provide effective solutions for UAV-assisted delivery systems \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of GA studies for optimizing flying vehicle routing-based applications.