## 6 Conclusions  
The present study has successfully developed a simulation model for a closed-loop vessel shipping system considering various uncertainties from weather and uncontrollable delays at destination ports. Furthermore, the developed optimization model embedded in \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{\begin{tabular}{c} **Vessel** \\ **Priority** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\#Vessel** \\ **SV** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Speed Factor** \\ **PS** \\ \end{tabular} } & \multicolumn{2}{c}{**\#Trips**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Marine Diesel Oil** \\ **Consumption (T)** \\ \end{tabular} } & \multicolumn{2}{c}{**Emissions (T)**} & \multirow{2}{*}{
\begin{tabular}{c} **Reduction (\%)** \\ \end{tabular} } \\ \cline{3-11}  & & **LV** & **SV** & **FS** & **BS** & **LV** & **SV** & **LV** & **SV** & **Total** & \(\mathbf{CO_{2}}\) & \(\mathbf{SO_{2}}\) \\ \hline C0 & FAFU & 2 & 2 & 1 & 1 & 168 & 190 & 2589 & 2741 & 5632 & 17,903 & 39 & - \\ \hline C4 & LVFU & 2 & 2 & 1 & 1 & 190 & 159 & 2924 & 2285 & 5503 & 17,494 & 39 & 2\% \\ \hline C1 & FAFU & 2 & 2 & 0.7 & 0.7 & 173 & 183 & 1892 & 1882 & 3988 & 12,676 & 28 & 29\% \\ \hline C5 & LVFU & 2 & 2 & 0.7 & 0.7 & 180 & 174 & 1971 & 1781 & 3963 & 12,600 & 28 & 30\% \\ \hline C2 & FAFU & 3 & 0 & 1 & 1 & 299 & - & 4596 & - & 4856 & 15,437 & 34 & 14\% \\ \hline C6 & LVFU & 3 & 0 & 1 & 1 & 299 & - & 4596 & - & 4856 & 15,437 & 34 & 14\% \\ \hline C3 & FAFU & 3 & 0 & 0.7 & 0.7 & 299 & - & 3272 & - & 3457 & 10,991 & 24 & 39\% \\ \hline C7 & LVFU & 3 & 0 & 0.7 & 0.7 & 299 & - & 3272 & - & 3457 & 10,991 & 24 & 39\% \\ \hline \hline \end{tabular}  
*: Reduction (%) = (Fuel consumption of C0 – Fuel consumption of the case)/Fuel consumption of C0.  
\end{table} 
Table 8: Fuel consumption and GHG emissions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{\begin{tabular}{c} **\#LV** \\ **\end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\#SV** \\ **Priority** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **
\begin{tabular}{c} **Vessel** \\ **Priority** \\ \end{tabular} } & \multicolumn{2}{c}{**Voyage Speed Factor** \\ \end{tabular} } & \multicolumn{2}{c}{**Product Volume (KT)**} & \multicolumn{2}{c}{**\#Vessel**} & \multicolumn{2}{c}{**Speed Factor**} \\ \cline{3-11}  & & & **FS** & **BS** & **Produced** & **Transported** & **LV** & **SV** & **FS** & **BS** \\ \hline C8 & 0–6 & 0–8 & LVFU & 0.7–1.6 & 0.7–1.6 & 6603 & 6580 & 5 & 0 & 0.7 & 0.7 \\ \hline C9 & 0–9 & 0–12 & LVFU & 0.7–1.6 & 0.7–1.6 & 9904 & 9860 & 8 & 0 & 0.7 & 0.7 \\ \hline C10 & 0–12 & 0–16 & LVFU & 0.7–1.6 & 0.7–1.6 & 12,942 & - & - & - & - & - \\ \hline \hline \end{tabular}  
\end{table} 
Table 9: Large production volume cases. the simulation model prescribes the optimal number of each type of vessel and forward and backward voyage speeds to minimize the total costs. To the best of our knowledge, this is the first simulation and optimization study to address the proposed closed-loop vessel dispatching problem. Further, we have devised a new vessel-use priority policy (LVFU) and compared it with the current policy (FAFU). The numerical studies showed that the optimal solution prescribed uses only three large vessels at the lowest voyage speed to transport the target product volume. It could save 26.8% (2,692,410 USD/year) of the total cost and reduce GHG emissions up to 39% compared with the current operating condition. Lowering voyage speed showed a significant impact on system performance. Additionally, a large vessel has good economic benefits from saving the voyage cost and port clues, which are greater than the disadvantage of the increased rental fee. Furthermore, even though the LVFU policy has a moderate effect, it would still be a recommendable operating option to be used with the current number of vessels and travel speed.