# Prediction of Ship-Unloading Time Using Neural Networks
## 3 Related Methods
### Stochastic Configuration Networks
Each ship-unloading record includes the following fields: BN (Berth Number), SN (Ship Name), RMN (cargo loaded), QRM (Quantity of Raw Materials), UD (Unloading Duration), ST (Starting Time of unloading), ET (End Time of unloading), SL1 (Ship Length), and SL2 (Storage Location in the stockyard). Additionally, there are other relevant fields not listed, such as berthing time, empty berthing time, and delay time, which can be appropriately incorporated into the calculations. Some unloading records are presented in Table 9.

A total of 70% of the collected data was utilized as the training set, while the remaining 30% served as the test set. The computational results are presented in Figure 14, which displays the prediction outcomes for 30, 50, and 100 ships. The experimental findings indicate that the SCN model achieves an average prediction accuracy of 87%, the RVFL model 85%, and the BP model 76%. Additionally, the running times for all three models are in seconds. Figure 14: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BN** & **SN** & **RMN** & **QRM** & **UD** & **ST** & **ET** & **SL1** & **SL2** \\ \hline
7 & FS8 & Ore fines 1 & 23,585 & 37 & 5 November 2018 15:00 & 7 November 2018 4:00 & 173 & OB24 \\
1 & MT & OYD-S/ONM- & 51,500 & 23 & 5 November 2018 9:00 & 6 November 2018 8:00 & 292 & OA16 \\
1 & BY & OHY-S & 90,000 & 34 & 7 November 2018 10:00 & 8 November 2018 20:00 & 320 & OA18 \\
2 & ZC258 & Ore fines 4 & 33,000 & 35 & 5 November 2018 9:00 & 6 November 2018 20:00 & 189 & OB32 \\
4 & BXH6 & dolomite & 1100 & 3 & 5 November 2018 13:00 & 5 November 2018 16:00 & 50 & OC23 \\
5 & JN9 & Limestone & 5005 & 18 & 6 November 2018 2:00 & 6 November 2018 20:00 & 110 & OC11 \\
2 & ZC258 & Ore fines 4 & 12,000 & 13 & 6 November 2018 9:00 & 6 November 2018 22:00 & 189 & OB16 \\
2 & BY & OHY-S & 90,000 & 34 & 7 November 2018 3:30 & 8 November 2018 20:00 & 320 & OA36 \\
4 & JN9 & Limestone & 5005 & 17 & 6 November 2018 9:00 & 7 November 2018 2:00 & 110 & OC26 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ship-unloading records. ## 5 Conclusions

The prediction of ship-unloading time is important for improving the efficiency of ship-unloading operations and reducing the delay cost for a steel plant. In this paper, three neural network-based methods-BP, RVFL, and SCN are proposed for the prediction purpose. The performance of these models is evaluated and tested using the benchmark data sets, which are from open databases and published literature. Finally, the three models are used for the prediction of ship unloading time. The experiment results indicate that the three models are very effective. The prediction accuracies of the SCN, RVFL, and BP are 87%, 85%, and 76%, respectively. That shows that our methods are effective and exhibit application potential. In this study, we developed neural networks to predict the unloading time at the raw materials terminal of a steel plant. This method holds significant practical implications, not only for the operation of the steel plant's raw materials terminal but also for similar industries and industrial processes that are heavily reliant on upstream operation times. We must acknowledge the limitations of our method. While the results presented in this paper are satisfactory, every method has its constraints. As discussed in the algorithm testing section, each algorithm requires meticulous design and debugging to fulfill its intended purpose. Furthermore, varying data sets can also influence the outcomes. Future research will concentrate on integrating data processing technologies with forecasting methods to address practical industrial challenges. Conceptualization: [PERSON] and D.L. ; data curation: [PERSON]; formal analysis: [PERSON]; methodology: [PERSON] and [PERSON]; experimental design and testing: [PERSON]; software: [PERSON] and Z.Y. ; supervision: [PERSON]; validation: [PERSON] and [PERSON]; writing original draft: [PERSON]; writing review and editing: [PERSON] and [PERSON].