## 5 Review of Case Studies: Flying Vehicle Routing Optimization
This study proposed a hierarchical facility location model to minimize the total costs of the system, taking into account establishment costs, drone procurement and usage costs. Two metaheuristic algorithms, GA and a hybrid GA, are developed and compared for solving the model. This study also examined the feasibility and profitability of investing in a drone delivery system. The findings provided valuable insights for companies considering establishing an aerial delivery system, addressing logistics, investment feasibility, resource requirements, and demand allocation. This paper suggested future research directions including considering capacitated models, fuzzy programming approaches, congestion models, and environmental sustainability assessments. [PERSON] et al. [64] presented a joint-optimization framework for addressing the UAV-routing and UAV-route scheduling problems in UAV-assisted delivery systems. They introduced mixed-integer linear programming models to capture the impact of incidental processes and varying the payload on travel time. To optimize the travel time, a hybrid genetic and simulated annealing (HGSA) algorithm is proposed for UAV-routing. The HGSA algorithm combined GAs and simulated annealing to achieve the optimal global position of customers while avoiding local optima. Additionally, a UAV-oriented MinMin (UO-MinMin) algorithm is introduced for UAV-route scheduling to minimize makespan. The UO-MinMin algorithm employs a UAV-oriented perspective to generate the route-scheduling order efficiently without compromising makespan quality. Extensive Monte Carlo simulations are conducted to evaluate the impact of the hybridization probability of GA and SA in the HGSA algorithm. The performance of the proposed algorithms is assessed using a set of benchmark instances, and they outperformed existing algorithms such as GA, PSO-SA, DE-SA, and HHO. The effectiveness of the UO-MinMin algorithm is demonstrated for various numbers of UAVs, surpassing base algorithms like MCT and OLB. [PERSON] and [PERSON] [65] addressed the problem of constructing a safe and efficient path for autonomous UAVs in target coverage scenarios. Various algorithms, including GA_1, GA_2, GA_3, GA_4, and GA_5, are proposed to overcome challenges such as collisions with terrain surfaces. The algorithms are tested in different 3D environments with rural and urban terrains. The results show that the GA_5 algorithm, which uses cluster centers based on collision points, outperforms the other algorithms by achieving a significant reduction in the required number of cost function evaluations. The convergence histories indicate that GA_4 and GA_3 also perform well. The accuracy of cluster centers plays a crucial role in the success of the Voronoi diagram in GA_3 and GA_4. On the other hand, GA_5 produces more accurate additional waypoints by considering only collision points, resulting in higher confidence levels in the results. Overall, the findings highlight the effectiveness of the proposed GA_5 algorithm in path planning for UAVs, particularly in avoiding collisions with terrain surfaces. [PERSON] et al. [27] proposed a coverage path planning method for fixed-wing UAVs based on an improved GA. The algorithm addressed the energy inefficiency issue caused by the conventional scan-type path planning approach. It utilized the good point set algorithm to generate a more uniform primary population and incorporates heuristic crossover and random interval inverse mutation operators to reduce the risk of local optimization. Simulation results demonstrated that the proposed algorithm outperforms the conventional GA in terms of solution quality, particularly with an increasing number of paths. The authors suggested exploring region segmentation algorithms for large areas and extending the coverage path planning to three-dimensional space in future research. [PERSON] et al. [66] explored the use of UAVs for emergency medical aid delivery.