## 5 Review of Case Studies: Flying Vehicle Routing Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **Problem Addressed** & **Objective(s)** & **Optimization Approach** & **Key Contributions** \\ \hline \multirow{2}{*}{[PERSON] et al. [9]} & Model for energy consumption in drone delivery considering & Minimize overall cost and delivery & \multirow{2}{*}{SA heuristic} & Improved performance compared to \\  & & sub-optimal solutions & time & identification of sub-optimal solutions \\ \hline \multirow{2}{*}{[PERSON] et al. [52]} & Optimization of homogeneous drone & \multirow{2}{*}{Minimize costs} & \multirow{2}{*}{Tailored branch-and-price algorithm} & Practical applicability, successful solution \\  & operations considering battery wear & & & \\  & and disposal effects & & & computation \\ \hline \multirow{2}{*}{[PERSON] et al. [74]} & Stochastic IP model for fast-time delivery considering uncertainty and traveling distance limitations & \multirow{2}{*}{Minimize total delivery costs} & \multirow{2}{*}{L-shape decomposition method} & Efficient solution computation, \\  & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [27]} & Coverage path planning for & Optimize coverage path planning for & \multirow{2}{*}{Improved GA for coverage path planning} & Improved solution quality for coverage \\  & fixed-wing UAVs using improved GA & & & path planning with increasing number of \\ \hline \multirow{2}{*}{[PERSON] et al. [66]} & Algorithm for efficient UAV & Develop an algorithm for efficient and safe UAV navigation from hospital to & \multirow{2}{*}{Path planning using capacitated VRP} & Improved efficiency and safety in medical \\  & delivery & medical emergency location & (CVPR) algorithm & emergency deliveries \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] [67]} & Optimization of VRP using UAVs & Optimize VRP (WR) using UAVs for & \multirow{2}{*}{MLP model and hybrid GA} & Efficient solution for VRP using UAVs with improved energy efficiency and \\  & Optimization of VRP using UAVs & energy-efficient and environmentally friendly deliveries & & environmental sustainability \\ \hline \multirow{2}{*}{[PERSON] et al. [68]} & Optimization of package delivery & Minimize delivery time for packages & \multirow{2}{*}{MIP model, FRGA, and RBGA genetic} & Efficient optimization of package delivery \\  & using fleet of drones & using a fleet of drones with different capabilities & algorithms & with improved solutions compared to \\ \hline \multirow{2}{*}{[PERSON] et al. [69]} & Optimization of UAV delivery paths & Minimize total cost and maximize & \multirow{2}{*}{NSA-II} & Efficient discovery of non-crowding \\  & for e-commerce and urban logistics & & paths for e-commerce and urban logistics & & Pareto optimal solutions with reduced \\  & logistics & & & computational complexity \\ \hline \multirow{2}{*}{[PERSON] et al. [53]} & Optimization of medical supply & Optimize delivery of medical supplies using drones during public health & \multirow{2}{*}{Bi-objective mixed integer programming model and modified NSGA-II algorithm} & Efficient and timely delivery of medical supplies during public health \\  & COVID-19 & & & NSGA-II algorithm & emergencies \\ \hline \multirow{2}{*}{[PERSON] et al. [70]} & Optimization of delivery routes using & Minimize delivery route makespan and carbon emissions by combining trucks and drones in a delivery system & \multirow{2}{*}{VRP with drones (VRPI) model and GA} & Reduced makespan and carbon emissions in delivery systems by incorporating drones \\ \end{tabular}
\end{table} 
Table 3: _Cont._These case studies collectively showcase the effectiveness of GAs in addressing the routing optimization challenges specific to flying vehicles. The application of GAs enables the consideration of multiple objectives, constraints, and trade-offs in the optimization process, resulting in improved performance and efficiency in the routing of flying vehicles. The assessment of the GA-based approach for multi-objective message routing in FVs involves the utilization of various performance evaluation metrics in existing studies.