## 5 Review of Case Studies: Flying Vehicle Routing Optimization
[PERSON] and [PERSON] [12] addressed the tri-objective two-echelon capacitated vehicle routing problem (2E-CVRP) for distributing perishable products. The objective is to minimize total travel cost, customer waiting times, and carbon dioxide emissions. The proposed model is formulated as a mixed-integer nonlinear programming problem and is transformed into a mixed integer linear programming problem using linearization methods. To solve the problem, they introduced a non-dominated sorting GA (NSGA-II) as a meta-heuristic approach. The algorithm is compared with the Lp-metric method in small-sized problems and with the multi-objective particle swarm optimization (MOPSO) algorithm in medium- and large-sized problems. The results demonstrate the efficiency and effectiveness of the NSGA-II algorithm in finding near-optimal solutions within a reasonable computational time. This study provided valuable insights into the optimization of vehicle routing for perishable products. It highlighted the importance of considering customer satisfaction through reduced waiting times and the reduction in carbon dioxide emissions for environmental sustainability. The comparison of different algorithms provides a benchmark for evaluating the performance of optimization approaches in solving the 2E-CVRP. [PERSON] et al. [19] addressed the multiple-depot vehicle distribution routing problem (MDVRP) in the context of perishable product distribution. This research focuses on finding the optimal route using a GA and optimizing parameter values through the use of a fuzzy logic controller (FLC). The objective is to minimize the risk of product spoilage by determining the shortest route for distribution. The combination of GA and FLC enhances the optimization process and improves the efficiency of the distribution system. The findings of the study demonstrated the effectiveness of the proposed approach in optimizing the MDVRP and reducing the risk of product deterioration. [PERSON] et al. [61] introduced a UAV-vehicle joint delivery model for logistics distribution in mountainous cities, addressing the challenges posed by complex terrain and limited UAV capabilities. The model aimed to improve delivery efficiency and reduce the total delivery route length. The proposed approach utilized a three-step route distribution method and employs a GA with end optimization to solve the problem. Simulation results demonstrate the effectiveness of the joint delivery model in solving logistics distribution problems in mountain cities. The future prospects of UAV technology and its potential advancements, such as increased load capacity and flight distance, are also highlighted. This study suggested further research directions, including expanding the joint delivery model to multiple UAVs and vehicles, considering additional factors like weather and signal interference, and exploring the application of the model in different urban areas. [PERSON] et al. [62] focused on the optimization of emergency logistics distribution using a collaborative delivery model with vehicles and UAVs. This study addressed the limitations of UAV-only delivery and proposes a model that considers factors such as start-up costs, waiting costs, and penalty costs. A GA is used to minimize the overall logistics distribution cost, including fixed costs, start-up costs, distribution costs, waiting costs, and penalty costs. The model is compared with other delivery models in a county-level district in Xi'an city, showing significant reductions in total cost compared to the vehicle-alone and UAV-alone models. The vehicle-UAV collaborative delivery model improved the distribution efficiency and customer satisfaction. However, they ignored to explore the impact of road congestion constraints and the scale of vehicles and UAVs on collaborative delivery in emergency situations. [PERSON] et al. [63] focused on the economics and logistics of a drone delivery system, specifically addressing the optimization of drone specificationsand the configuration of launch and recharge stations.