## 3 Hybrid Algorithm--RSO-IGA
However, the total distances of ABC algorithm and GA-TS algorithm were shorter than those of the H university delivery plan, indicating no inherent correlation between delivery frequency and total routing distance. The RSO-IGA algorithm achieved the shortest routing configuration within a competitively compressed timeframe while maintaining a high average container utilizatio

Figure 15: (**a–e**) are the packing schemes of the first, second, third, and fourth paths of the optimal solution of the artificial bee colony algorithm, respectively. (**f**) is the schematic diagram of path planning. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Path Order** & **Route** & **Mileage** \\ \hline
1 & 0,1,7,11,14,8,29,9,10,12,13,0 & 3714 m \\
2 & 0,2,3,4,5,6,15,16,0 & 2262 m \\
3 & 0,17,18,19,20,21,22,23,24,25,26,27,28,0 & 4148 m \\
4 & 0,30,31,32,33,34,0 & 2223 m \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A certain day order distribution plan for H University. cargo stability during transportation. Additionally, it ensures the stability of parcels within the vehicle, making it a superior solution for campus logistics optimization. ## 5 Conclusions

In this paper, the IGA is combined with the residual space-optimized three-dimensional bin packing algorithm to propose the RSO-IGA. The parameters required for this algorithm are set after validation through computational examples. The RSO-IGA algorithm is used to solve the optimal path scheme and the corresponding packing scheme of the actual data of H University on a certain day. Compared with the original path scheme of H University and the other two algorithms, the advantages and disadvantages of the path scheme and the average loading rate are evaluated. Finally, the real-world route maps displayed on Gaode Maps demonstrate that the RSO-IGA algorithm can yield higher-quality solutions, providing a more efficient delivery plan for the campus distribution center of H University. While this study considers geometric constraints and the LIFO constraint in three-dimensional bin packing, real-world scenarios may involve more complex constraints. Therefore, future research could explore more intricate packing constraints. **Author Contributions:** X.Y. : resources, formal analysis, methodology, software, funding acquisition, and writing--original draft. Z.Y. : writing--review and editing. Y.L. : methodology, project administration, and funding acquisition. Y.C. : investigation and resources. A.G.: resources and formal analysis. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Number of Trips** & **Mileage** & **Running Time** & **Average Loading Rate** \\ \hline H University delivery plan & 4 & 10,347 m & 0 s & 75.65\% \\ Artificial bee colony algorithm & 5 & 9054 m & 2453 s & 60.52\% \\ GA-TS algorithm & 5 & 8810 m & 3754 s & 60.52\% \\ RSO-IGA & 4 & 7501 m & 2284 s & 75.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of various methods for placing an order on a certain day at H University. Figure 16: (**a–e**) are the packing schemes of the first, second, third, and fourth paths of the optimal solution of the GA-TS algorithm, respectively. (**f**) is the schematic diagram of path planning. **Funding:** This work is supported by the following projects: the Research Starting Fund of Wuhan Vocational College of Software and Engineering (KYQDJF2023008) and the Research Starting Fund of Hubei University of Technology (BSQD2020009). **Data Availability Statement:** The original contributions presented in this study are included in this article. **Conflicts of Interest:** The authors declare no conflicts of interest. ## Appendix A

_Appendix A.1_