## 4 Comparison
### Comparison of Charging Algorithms  
According to the discussion in Section 3.2, in this subsection different charging strategies in drone-based delivery systems are compared with each other. Table 4 lists the objectives, advantages, limitations, innovative features, and constraints considered in the optimization process and the performance metrics of different charging strategies. As provided, the main objective of charging strategies is to maximize the delivery coverage area, drone flight time, and minimize the number of required CSs according to the distribution of delivery destinations by adjusting the positions of CSs to reduce the deployment cost. \begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Main Idea** & **Advantages** & **Limitations** \\ \hline RL-TDCD [42] & A combined delivery method of truck and drone is proposed where clustering of the designated delivery point and routing plan is designed. & This algorithm has shorter execution time compared to the other heuristic approaches and reduces the overall drone energy consumption. & The problem formulation ignores the time required for charging or swapping batteries of drones as well as the loading/unloading parcel time. \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Evaluated Metrics** & **Performance Objective** & **Innovative Features** \\ \hline A-Ptr-Net [6] & Time of customer nodes, trajectory optimality gap, effect of attention mechanism, energy gap. & Minimizing route distance by finding optimal route path. & Attention mechanism on the decoder end to adjust the trajectory information. \\ \hline JRCS [12] & Average delivery time, algorithm computation time, delivery area coverage ratio, drone travel distance, the number of customers served, and energy consumption. & Maximizing drone flight distance with respect to the drone CS. & Maximum flight distance and safe flight distance for the drone-based delivery system is considered for problem formulation. \\ \hline CRP-TD [15] & Instances, number of drones, drone speed. & Minimizing the delivery time. & K-means clustering and nearest neighbor strategy to generate an initial solution. \\ \hline EHTDDP [16] & Instances, service time, number of distribution centers. & Minimized delivery time and cost. & MIP formulation yields better linear relaxation bounds than other benchmarks. \\ \hline FSTSP-STT [41] & Variation of travel time, average delivery time. & Minimizing the stochastic travel time of the drone for delivery. & DQN and A2C are combined and used to address the problem of dimensionality. \\ \hline RL-TDCD [42] & Average travel distance, travel time, average running time, number of clusters, convergence rate. & Minimizing the average travel distance & Provide last mile parcel delivery using a combined truck and drone system. \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of trajectory planning algorithms in drone routing in terms of evaluated metrics, performance objective, and innovative features. According to Table 5, most methods ignored the environmental effects such as the presence of wind, mountain, and external obstacles, to calculate the realistic drone flight time, which is crucial to obtain the optimal locations of CSs. Additionally, an accurate energy consumption model considering drone type, aerodynamics, payload size, and trajectory is required to estimate the optimal flight time. Next, most algorithms, except CBPTCS [11], ignore the restriction for placing the CSs near restricted areas such as rivers, mountains, etc. In the optimization process, all the considered algorithms, except CDNC [51], consider that the CSs have sufficient resources, such as availability of charging pads, and spare fully-charged batteries. Moreover, they ignored the failure possibility of CSs. This assumption may be inaccurate in real-life scenarios, as CSs have limited resources to allocate to drones.