## 5 Review of Case Studies: Flying Vehicle Routing Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Application Domain** & **Optimization Technique** & **Performance Metrics** & **Results** \\ \hline [PERSON] and [PERSON] [65] & UAV path planning & GA & Safe path construction & Algorithms overcome challenges such as collisions with train surfaces and provide safe and efficient path planning for autonomous UAVs \\ \hline [PERSON] et al. [27] & Fixed-wing UAVs & GA & Energy efficiency, solution quality & Improved energy efficiency compared to conventional scan-type path planning approaches \\ \hline [PERSON] et al. [66] & Emergency aid delivery & Capacitated VRP (CVRP) & UAV navigation efficiency & Efficient and safe UAV navigation from a hospital to the location of a medical emergency \\ \hline [PERSON] and [PERSON] [67] & VRFD & GA & Energy efficiency, environmentally & Tailored GA for optimizing UAV deliveries with battery-powered drones \\ \hline [PERSON] et al. [68] & Drone-assisted delivery & GA & Makespan, carbon emissions & GAs outperform MIL approach, reducing both makespan and carbon emissions \\ \hline [PERSON] et al. [69] & E-commerce, logistics & NSGA-II & Multi-objective path planning & Genetic algorithm generates sparse and well distributed non-dominant solutions within a short time \\ \hline [PERSON] et al. [53] & Medical supply delivery & Modified NSGA-II, mixed-integer programming & delivery time, safety, resource savings & Simultaneous pick-up and delivery mode achieves shorter delivery times, improves safety, and saves more resources \\ \hline [PERSON] et al. [70] & Delivery optimization & GA & Carbon emissions, makespan & GA reduces both makespan and carbon emissions, incorporating drones in the delivery process \\ \hline [PERSON] et al. [71] & Parcel delivery & GA & Speed, solution quality & GA outperforms other algorithms in terms of speed and solution quality \\ \hline [PERSON] et al. [72] & Last-mile delivery & GA & Energy consumption, carbon emissions & Mixed-integer green routing model maximizes sustainability benefits, reducing energy consumption and carbon emissions \\ \hline [PERSON] et al. [73] & Pickup and delivery & GA & Optimization quality & Tailored GA generates high-quality solutions for pickup and delivery optimization with UAVs \\ \hline [PERSON] et al. [9] & Drone energy consumption & SA & Energy consumption optimization & Cost function considers drone reuse, identifies sub-optimal solutions in real-life scenarios \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **Application Domain** & **Optimization Technique** & **Performance Metrics** & **Results** \\ \hline [PERSON] et al. [52] & Drone operations & Branch-and-price algorithm & Battery wear, disposal effects & Branch-and-price algorithm optimizes \\  & & & & homogenous force operations, considering \\  & & & & battery wear and disposal effects \\ \hline [PERSON] et al. [74] & Delivery optimization & L-shape decomposition method & Uncertainty handling, delivery conditions & Three-stage stochastic IP model considers uncertainties in delivery conditions \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._Based on the provided Table 4, it is evident that the GA-based studies cover a wide range of application domains, including FANETs [6], UAV path planning [54], UAV networks [55], VRPs [56], logistics distribution [58], emergency logistics [59], drone delivery systems [18], and medical supply delivery [71]. This indicates the diverse areas in which GA-based optimization techniques are being applied to enhance efficiency and effectiveness. The optimization techniques employed in these studies vary, with common approaches including GAs, A-Star algorithm, PSO, ACO, and MILP. These techniques are well suited for addressing complex optimization problems and have been adapted and enhanced to tackle specific challenges in each application domain.