# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 5 Comparative Analysis of Optimization Results Under Different Conditions
Based on the push-pull time, installation time, and push-pull sequence, the construction timeline is plotted as a Gantt chart, as shown in Figure 17. In this chart, the red segments represent push-pull time, while the green segments represent  
Figure 14: Passage chart of cruise path planning time. Figure 15: Single cabin (35,1) push–pull path diagram. installation time. Based on the calculation results, the optimal path for the cabin (16,1) is plotted in Figure 18. The path planning algorithm calculates the shortest total transport time for the cabin as 16,304.78 t.  
From the path planning result in Figure 18, it can be observed that the optimized path successfully avoids all obstacles, such as other cabins and bulkheads, and completes the push-pull operation via the shortest path. Additionally, there are no instances where a cabin is blocked by another due to sequencing issues. The Gantt chart in Figure 17 indicates that at any given time, the maximum number of push-pull operations is one, and the maximum number of installations is three. All cabins are processed in the prescribed sequence, with no overlap between where a subsequent cabin begins installation and where a previous one is completed. The distance between concurrently installed cabins meets the requirement of being at least two cabin widths apart, thus fulfilling the design requirements. It can be seen that in most cases, three cabins are installed concurrently, with two cabins installed simultaneously in some instances. Only at the beginning is there a single cabin  
Figure 16: Pushing sequence diagram. Figure 17: Passage chart of cruise path planning time with limited push–pull and installation personnel number. being installed alone. This indicates that human resources are being utilized efficiently, and the idle time caused by the installation speed of the cabins during the push-pull operation is significantly reduced. With a reduction in workforce, the shortest total transport time of cabins increased from 14,524.20 t to 16,304.78 t. However, the increase in time was relatively small, while the number of workgroups decreased from seven to four, indicating a substantial reduction in workforce. This suggests that the design scheme is more efficient and rational. In this experimental section, various human resource allocation scenarios were considered, and two typical cases, unrestricted and restricted resource availability, were analyzed in detail. By examining the transportation path length and obstacle-avoidance efficiency of a single modular cabin as a case study, the path optimization capability of the proposed algorithm was validated. Furthermore, comparative analyses of the algorithm's performance under different experimental conditions revealed that although transportation time increased slightly with a reduction in human resources, the overall efficiency did not significantly deteriorate, indicating good adaptability and robustness of the algorithm under resource-limited conditions. It should be noted, however, that while the improved A-Star algorithm proposed in this paper effectively addresses the current optimization challenges for cabin transportation paths, certain limitations remain. In practical implementation scenarios, dynamic variations in the construction environment, such as the emergence of temporary obstacles and fluctuations in the performance of pushing equipment, could hinder the direct application of the proposed solution. Consequently, future research should investigate more flexible, real-time optimization approaches, such as integrating dynamic heuristic algorithms with multi-agent collaboration, to enable real-time adjustment of transportation paths and thus enhance practical applicability.