## 5 Review of Case Studies: Flying Vehicle Routing Optimization
This modified algorithm incorporates double-layer coding to enhance its performance in finding optimal solutions. Multiple sets of data experiments are conducted to evaluate the performance of the proposed model and algorithm. The results demonstrate that the simultaneous pick-up and delivery mode achieves shorter delivery times, improves safety, and saves more resources compared to separate pick-up and delivery modes. Additionally, sensitivity analysis is performed by varying certain parameters, providing valuable insights for the management of medical supply delivery using drones. Overall, the proposed approach shows promising results in meeting the specific requirements of medical supply delivery during public health emergencies. [PERSON] et al. [70] examined the feasibility and environmental advantages of using drones for delivery. It introduces the VRP with drones (VRPD) model, which aims to minimize both the delivery route's makespan and carbon emissions. The model incorporated the collaboration between trucks and drones, where each truck is equipped with a drone to enhance delivery efficiency. The paper presented a mathematical formulation for the VRPD and proposes the use of the NSGA-II to solve it. The algorithm's performance is evaluated using different-scale problems, and the quality of the solutions is assessed using hypervolume and spacing metrics. The results demonstrated that the algorithm generates high-quality solutions, as indicated by favorable hypervolume and spacing values. Through significance tests, the study confirmed the benefits of using drones in terms of reducing both makespan and carbon emissions. The findings underscore the significant difference achieved by incorporating drones in the delivery process. [PERSON] et al. [71] focused on the scheduling problem of parcel delivery using drones, where customers need to be assigned to both takeoff points and drones. A GA is proposed as a solution method to efficiently find a near-optimal solution. Experimental results demonstrated that the proposed GA outperforms an integer programming (IP) solver and other GAs in terms of speed and solution quality. The effectiveness of the GA is attributed to its optimized crossover and mutation operations. It is important to note that this study considers a scenario where drones carry only one parcel. The authors ignored the exploration of the problem to drones carrying multiple parcels. [PERSON] et al. [72] investigated the environmental and cost-saving implications of using unmanned aerial vehicles (UAVs) for last-mile parcel deliveries. The focus is on analyzing the impact of UAVs on CO2 emissions and cost. A mixed-integer green routing model is proposed to maximize the sustainability benefits of UAV usage. The model considers factors such as energy consumption and carbon emissions. To solve the complex model, a GA is developed. Extensive experiments are conducted to validate the analytical model and the solution algorithm. The results demonstrated that routing and delivering packages with UAVs can lead to energy savings and a reduction in carbon emissions. These findings strongly support the argument that utilizing UAVs for last-mile logistics is not only cost-effective but also environmentally friendly. [PERSON] et al. [73] addressed the pickup and delivery optimization problem with a time window and intermittent connectivity network using UAVs. The NSGA-II algorithm was successfully applied, considering UAV-specific constraints like refueling. A novel representation of individuals and associated heuristics and algorithms were introduced to improve the solution generation, crossover, and mutation processes. Experimental results showcased the effectiveness of the NSGA-II approach in achieving high-quality results for the PDPTW-UAV problem, with and without the consideration of refueling constraints.