# Ship Type Selection and Cost Optimization of Marine Container Ships Based on Genetic Algorithm
## 3 Case Study Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ship Type**} & **Allocated** & **Transport** & **Service** & **Fixed** & **Total** & **Carbon** & **Operating** & **Carbon** \\  & **Cargo** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** & **Cargo/CNY** & **Emissions** & **Cost per Unit of** & **Emissions** \\  & **Volume (t)** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** & **Cost/CNY** \\ \hline
1 & 1200 & 8250 & 3600 & 800 & 12,650 & 342 & 10.54 & 0.285 \\
2 & 1800 & 150.1 & 493.9 & 850 & 13,000 & 930.6 & 7.22 & 0.517 \\
6 & 100 & 1100 & 250 & 500 & 1850 & 54.2 & 18.5 & 0.542 \\
7 & 900 & 7290 & 1980 & 750 & 10,020 & 158.4 & 11.13 & 0.176 \\ Total & 4000 & 8682.5 & 4982.7 & 2900 & 37,520 & 1485.20 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal ship type selection and allocation scheme. tion. The genetic algorithm model designed in this paper reaches the optimal solution by the 6 th generation, and after multiple training sessions, with 10 generations of iterations, the accuracy of the optimal solution reaches 93.7%. Throughout the algorithm's execution, both bi-level objectives--minimizing operational costs and minimizing carbon emissions--converge simultaneously in successive iterations, showcasing the substantial application potential of this algorithm in addressing practical issues related to ship type selection and transport task allocation in shipping companies. The trend of carbon emissions is shown in Figure 4.  
In recent years, with the continuous increase in human resource costs, the transportation service cost per unit of goods has inevitably been rising annually. Therefore, the unit added-value service cost for ship types can be set to increase by 10% per year. Figure 5 shows the growth of unit service costs over the next three years. By applying the algorithm for calculation, the optimal operating cost change under this condition can be obtained in Figure 6 as follows: when the cost increases by 10% in the first year, the total operating cost increases by CNY 38,553; when the cost increases by 10% in the second year, the total operating cost increases by CNY 39,697; when the cost increases by 10% in the third year, the total operating cost increases by CNY 40,942. It can be observed that with a 10% annual increase in unit value-added service costs, the total operating cost growth rates each year are 2.75%, 2.97%, and 3.14%, respectively. The contribution rates of unit value-added service costs to the total cost growth are 27.5%, 29.7%, and 31.4%, respectively. From these data, it can be concluded that unit value-added service costs will be the primary factor driving cost increases for shipping companies in the future. Therefore, in future development, shipping companies can invest a portion of fixed capital in purchasing industrial automation equipment, such as automated loading and unloading systems, and automatic sorting and packing robotic arms. This will help reduce the total number of laborers, improve labor efficiency, and lower overall operating costs. Figure 4: Trend in carbon emissions.