## 5 Results Analysis
### Comparative Analysis with the FCFS Rule  
In Table 17, GA_GA saves 9.42 h compared to GA_FCFS, and GA_FCFS saves 14.60 h compared to [M]. In fact, the results of the ten examples in Figure 8 and Table 18 also exhibit similar characteristics to Table 17 (Due to limitations on space, this paper only takes Table 17 as an example). In other words, in these ten examples, the Obis of GA_GA and IG+ECTT are lower than GA_FCFS; however, if the hub allocation results of GA_GA are used with the FCFS rule, the Obis are higher than GA_FCFS. Further analysis for intermediate data (e.g., Figure 9) reveals that the average time consumed in operation (2)-1 by GA_GA is significantly longer than by GA_FCFS while the average times consumed in operations (1), (2)-2, (3), (4)-1, (4)-2, and (5) are all shorter than by GA_FCFS. These results indicate that transshipment sequence selection at upstream stations is the key reason that GA_GA and IG+ECTT outperform GA_FCFS. Although it increases the waiting time for partial equipment at upstream stations, it reduces the transport time, the loading and unloading time, and the waiting time at downstream stations, thereby enhancing overall multimodal transport efficiency. In Figure 8, the Obis of GA_GA, IG+ECTT, and GA_FCFS show an upward trend with equipment quantity increasing, while the Gaps of IG+ECTT and GA_FCFS first increase and then decrease. The maximum Gaps of IG+ECTT and GA_FCF both occurring at 11,000 units, are 3.17% and 4.20%, respectively. Compared to GA_FCFS, GA_GA, and IG+ECTT save 33.68 h and 8.58 h, respectively. This indicates that for a given multimodal transport network, transport demand that is too high or too low can affect the optimization effects of transshipment sequence selection. In practice, adjusting transshipment sequences may increase the management pressure of upstream stations, such as requiring larger transshipment buffers or increasing storage and handling management costs. Therefore, comprehensively considering various transshipment sequencing rules in practice is essential. For instance, a better transshipment sequence should be considered if decision-makers wish to minimize the makespan. Conversely, if they want to reduce the management difficulty of transshipment operation while meeting time constraints, the FCFS rule might be a more favorable choice. By examining [C1] and [C2] (Table 18), it is found that the Obis of GA_GA, IG+ECTT, and GA_FCFS, as well as the Gaps of IG+ECTT and GA_FCFS, all show decreasing trends with the improvement of transshipment efficiency. Among them, the Gaps of IG+ECTT and GA_FCFS reach their minimums in the third example of [C2] (see row 8 of Table 18), which are 0.54% and 0.93%, respectively. Compared to GA_FCFS, The Obis of GA_GA and IG+ECTT save 4.16 h and 1.75 h, respectively. This indicates that the lower the transshipment efficiency of the multimodal transport network is, the more effective the transshipment sequence selection may be. It can also be found that when the increases in transshipment efficiency are the same, the Gaps of GA_FCFS in [C1] are larger than [C2]. For example, in rows 3 and 6 of Table 18, although the total transshipment efficiency of the multimodal transport network is 307 units/h in both examples, the Gaps of GA_FCFS are 3.03% and 1.61%, respectively. In row 3, the Obis of GA_GA and IG+ECTT save 23.76 h and 6.25 h compared to GA_FCFS, respectively; whereas in row 6, savings are 9.76 h and 3.60 h, respectively. This indicates that the more significant differences in transshipment efficiency between upstream and downstream stations, the more impactful the transshipment sequence selection. Additionally, in the ten examples of sensitivity analysis, the average Cpu of GA_FCFS is approximately 25 s, outperforming GA_GA (1138 s) and IG+ECTT (36 s).