## 6 Conclusions  
In this paper, we address a comprehensive CDP that integrates eight types of common transportation requests, the availability of empty containers across multiple depots, the Stay-with and Drop-and-pull modes for handling composite requests, and the Depot-turn and Street-turn strategies for repositioning empty containers. A novel MILP model based on the DAOV graph is proposed to minimize the costs associated with fuel consumption, carbon emissions, and truck waiting times, while simultaneously optimizing decisions regarding empty container allocation, truck dispatching, and route planning. Due to the proposed MILP only solving small-scale instances optimally with Gurobi, an improved LNS algorithm, incorporating the \"Sequential insertion\" and the \"Solution re-optimization\" methods, has been developed to handle industrial-sized instances. The \"Sequential insertion\" method is responsible for converting the solution into multiple truck routes, while the \"Solution re-optimization\" method is used to refine the quality of solutions and ensure the feasibility of the truck routing plan. The performance of the proposed algorithm has been validated by extensive experiments with varying scales. While Gurobi can solve the MILP model to optimality for small-scale instances, it can only obtain a near-optimal solution for medium-scale instances within 1 h. Although the average objective value of the LNS for all small-scale and medium-scale instances is 0.26% higher than that of the MILP, it consumes significantly less solving time. Additionally, LNS obviously outperforms GA in terms of both the objective values and solution time for large-scale and super-large-scale instances. In particular, the LNS has an average solving time that is 107.78 s shorter than GA, along with an average objective value 3.86% lower than that of GA for super-large-scale instances. The stability analysis of LNS and GA also demonstrates its ability to provide stable solutions for the proposed CDP. Sensitivity analysis reveals the speculation that the number of depots and empty containers is a key factor in optimizing the container transportation network. However, as the number of depots or empty containers increases, the marginal impact on the total cost first rises and then diminishes. These findings indicate that achieving an optimal \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Instance**} & \multicolumn{2}{c}{**Cost of Depot and Empty Container Distribution**} & \multicolumn{2}{c}{**Cost Savings from R/R (\%)**} \\ \cline{2-6}  & R/R & **U/U** & **F/F** & **U/U** & **F/F** \\ \hline
30 & 1523.2 & 1482.5 & 1425.9 & 2.67 & 6.39 \\
34 & 1912.8 & 1853.7 & 1782.0 & 3.09 & 6.84 \\
38 & 1366.2 & 1345.4 & 1289.5 & 1.52 & 5.61 \\
42 & 3441.7 & 3325.2 & 3156.1 & 3.38 & 8.30 \\
46 & 7900.5 & 7692.3 & 7005.8 & 3.65 & 11.32 \\
50 & 15,661.1 & 15,125.8 & 13,739.6 & 3.80 & 12.27 \\ \hline \end{tabular}
\end{table} 
Table 9: Effect of the distribution of depots and empty containers. balance between depots and empty containers is essential for minimizing transportation costs. Furthermore, the uniform and focused distribution of depots and empty containers can reduce, respectively, 3.02% and 8.46% of the average total cost, as they help reduce the transport distance for large quantities of empty containers between depots and customers, as well as the distance for empty trucks departing from or returning to the depots. Note that this study does not address the costs of depot construction and empty container purchases associated with their quantities, which could impact the payback period and operational efficiency, and should be determined from a planning perspective. It is worthwhile to investigate these factors when planning an efficient drayage system. Additionally, with the development of e-commerce drayage platforms, dynamic orders will gradually become an indispensable element, which poses a challenge to the online optimization of the comprehensive CDP. Conceptualization, X.Y. ; methodology, X.Y. ; software, C.H.