## 5 Review of Case Studies: Flying Vehicle Routing Optimization
The proposed algorithms included a GA for solving the traveling salesman problem with respect to priorities and obstacle avoidance, a projective algorithm for finding the shortest paths around obstacles, and a quadratic program for generating minimum-snap polynomial trajectories while ensuring the avoidance of static obstacles. These algorithms enabled efficient motion planning in both 2D and 3D environments and have been implemented in real-time scenarios. [PERSON] et al. [58] focused on UAV path planning and proposes two parallel optimization algorithms: the improved GA (IGA) and the particle-swarm-optimization-based ant colony optimization algorithm (PSO-ACO). The goal is to solve the TSP for UAVs and find optimal path planning solutions. The TSP model is established, considering UAVs as traveling salesmen and mission targets as cities. The IGA and PSO-ACO algorithms are applied to solve the TSP model, providing effective and reasonable UAV path planning schemes. Experimental results show that the proposed algorithms outperform a contrast approach and offer more rational and effective solutions for UAV path planning. The study highlighted the importance of UAV path planning for successful UAV missions and contributes to the development of UAV path planning techniques. [PERSON] et al. [59] presented a coverage path-planning algorithm for quadrotors in a 2D workspace with obstacle avoidance. The algorithm consists of two steps: the first step uses the RRT*FN algorithm to find optimal paths between points and their neighbors, while the second step connects these points to form the overall shortest path using GAs. The algorithm also addresses energy consumption by providing a scalable solution using the savings technique. A discrete model of the map is generated, and points of interest (POIs) are defined based on this discretized map. The proposed algorithm offers an effective solution for coverage scenarios with obstacle avoidance and energy efficiency. Numerical simulations demonstrate promising results for the algorithm's effectiveness. [PERSON] et al. [60] investigated the effectiveness of implementing unmanned aerial delivery vehicles in delivery networks. The focus is on comparing the performance of a truck-drone network with standalone truck or drone delivery systems in terms of overall delivery time and energy consumption. The objectives included examining the time and energy associated with the truck-drone network, proposing an optimization algorithm to determine the optimal number and locations of launch sites, and developing mathematical formulations for estimating the optimal number of launch locations and total delivery time. The algorithm presented in their paper utilized K-means clustering to find optimal launch locations and a GA to solve the truck route as a TSP. The optimal solution is determined by minimizing the cost associated with a parabolic convex cost function. The results show that in tandem delivery efforts outperform standalone systems, and having multiple drones per truck leads to energy and time savings. This paper also provided closed-form mathematical solutions that serve as estimators for the optimal number of launch locations and delivery time. [PERSON] and [PERSON] [22] focused on the IRP and its relevance in addressing fuel consumption and environmental impact in the transportation sector. They proposed a bi-objective model for IRP that incorporates fuel consumption and pollutant emissions as separate objectives. The model also considers a heterogeneous fleet composition, making it more realistic for real-world applications. The study highlighted the importance of optimizing fuel consumption to improve economic efficiency and mitigate environmental issues related to transportation. The proposed model offered a novel approach to solving IRP and shows promising results compared to other methods. By addressing the challenges associated with the fuel consumption and environmental impact, this study contributed to the field of transportation and logistics.