## 5 Results and Discussion
### Intermodal Scheduling Case Study
#### 5.2.2 Weighting Factors' Impact on Decision-Making  
Changes in the weight coefficients of the objective function can significantly affect transport decision outcomes. In this experiment, all initial parameters are kept constant, with only the weight coefficients varied. The IGWOHHO algorithm is executed ten times for each setting to determine the optimal solution that minimizes the objective value, with the results presented in Figure 14. A higher weight coefficient indicates a greater emphasis on minimizing the corresponding sub-objective. Different weight configurations lead to varying levels of total cost, carbon emissions, and transport time, reflecting distinct optimization priorities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Objective Function Value** & **Transport Route** & **Type of Transport** & **Solution Time (s)** \\ \hline GWO & 0.2739 & O-3-4-11-15-20-26-27-D & b–b-a-b-c-c-b-b & 142.29 \\ WOA & 0.3418 & O-1-6-10-14-15-20-26-27-D & b-b-b-a-b-b-c-c-b-b & 174.35 \\ SSA & 0.2672 & O-1-7-8-12-23-30-31-33-D & b-b-b-b-c-c-b-b-b-a & 108.57 \\ DBO & 0.3183 & O-1-7-8-12-17-19-21-26-27-D & b-b-b-b-c-a-a-a-a-b-b & 187.03 \\ HHO & 0.2711 & O-1-7-8-13-19-21-25-28-32-D & b-b-b-b-b-c-a-b-b-b-b & 156.25 \\ IGWOHHO & 0.2405 & O-2-5-10-14-15-20-26-27-D & b-b-b-b-a-c-c-b-b & 119.56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Solution results of different algorithms.  
Figure 13: Comparison of total cost, carbon emission, and time across algorithms.  
Among the results, three representative solutions with objective values of 0.2162, 0.2322, and 0.4682 are selected for further analysis. Table 7 provides the details of the corresponding transport schemes for these selected solutions. Figure 15 visualizes the comparative performance of these schemes, focusing on total cost, carbon emissions, and transport time. This comparison highlights the effects of different weight settings on the algorithm's ability to balance cost efficiency, environmental sustainability, and time efficiency.  
The analysis of the results in Table 7 and Figure 15 reveals that varying the weightings of the objective function leads to different transport schemes. When cost is prioritized,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & \begin{tabular}{c} **Weighting Factor** \\ **(\(\omega_{1}\), \(\omega_{2}\), \(\omega_{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Objective Function Value** \\ \end{tabular} & \
\begin{tabular}{c} **Transport Route** \\ \end{tabular} &
\begin{tabular}{c} **Type of Transport** \\ \end{tabular} \\ \hline Option 1 & (0.6, 0.2, 0.2) & 0.2162 & O-3-4-11-15-20-26-27-D & b-b-a-c-c-c-b-b \\ Option 2 & (0.2, 0.6, 0.2) & 0.4682 & O-2-5-10-14-16-21-25-28-32-D & a-b-b-b-a-a-b-b-b-b-b-b \\ Option 3 & (0.2, 0.2, 0.6) & 0.2322 & O-2-5-10-14-16-20-26-27-D & a-b-b-b-b-b-a-c-b-b \\ \hline \hline \end{tabular}
\end{table}
Table 7: Information on transport programs with different weighting factors.  
Figure 14: Optimal values with different weight coefficients.  
Figure 15: Targeted results of different programs.  
as in option 1, the total cost is minimized, but both transport time and carbon emissions increase. In contrast, option 3, which emphasizes environmental sustainability, reduces carbon emissions, though it results in higher costs and longer transport times. By examining the effects of objective function weighting on optimization outcomes, customized transportation strategies can be developed to support decision-makers with diverse priorities. Adjusting the weightings provides flexibility, allowing for a dynamic balance between cost efficiency and environmental sustainability. This approach offers decision support for multi-scenario intermodal transport scheduling.