# Prediction of Ship-Unloading Time Using Neural Networks
###### Abstract  
The prediction of unloading times is crucial for reducing demurrage costs and ensuring the smooth scheduling of downstream processes in a steel plant. The duration of unloading a cargo ship is primarily determined by the unloading schedule established at the raw materials terminal and the storage operation schedule implemented in the stockyard. This study aims to provide an accurate forecast of unloading times for incoming ships at the raw materials terminal of a steel plant. We propose three neural network-based methods: the Backpropagation Neural Network (BP), the Random Vector Functional Link (RVFL), and the Stochastic Configurations Network (SCN) for this prediction. This issue has not been previously researched using similar methods, particularly in the context of large-scale steel plants. The performance of these three methods is evaluated based on several indices: the Root Mean Square Error (RMSE), the quality of the best solution, convergence, and stability, which are employed for predicting unloading times. The prediction accuracies achieved by the BP, RVFL, and SCN were 76%, 85%, and 87%, respectively. These results demonstrate the effectiveness and potential applications of the proposed methods. prediction; ship-unloading; neural networks; steel plants +
Footnote †: journal: Applied  
0000-0001-9900-0001-9900-0000]D. Dehghani 0000-0001-9900-0001-9900]D. Dehghani 0000-0001-9900-0001-9900-0001-9900-0001-9900-0001-9900-0001-99000-0001-99000-0001-99000-0001-99000-0  
three ship unloaders to facilitate the unloading operations. The conveyor system is installed throughout the entire port area, the stockyard, and certain production areas to enable the bidirectional transfer of raw materials and products among these locations. When a ship arrives at the raw materials terminal, a berth and one or more ship unloaders are assigned to it for the unloading operation. Simultaneously, storage space must be designated for the unloaded raw materials in the stockyard. Accurate prediction of ship-unloading times can create buffer space for subsequent dock-unloading schedules and storage space allocation. This improvement facilitates the execution of related operations, reduces ship delay costs, and enhances economic efficiency, making it a critical factor in port operations. The prediction methods have become a prominent area of study in both industrial applications [3; 4] and academic research [5; 6]. These methods can be broadly categorized into three groups: statistical methods [7; 8; 9], mechanistic methods [10; 11], and data-driven methods [12; 13]. Statistical methods operate under the assumption that the observations of the predicted object follow specific statistical distributions, utilizing statistical theory to derive prediction results. In contrast, data-driven methods rely entirely on data, employing machine learning techniques to generate predictions. Mechanistic methods assume that the predicted object adheres to certain physical or chemical laws, including thermodynamics, dynamics, and fluid mechanics, and they derive predictions through the application of relevant mechanistic equations. However, data-driven methods have gained popularity due to their user-friendliness and lower implementation costs in industrial settings. As far as we know, unloading time prediction is rarely studied for bulk carriers. However, there are several analogous studies in other industries. For instance, [PERSON] et al. [14] developed a fuzzy sequence-to-sequence network for forecasting unloading times in earthmoving projects. [PERSON] and [PERSON] [15] developed a modularized simulation method (MSM) for forecasting delivery times of priority lots in a 300 mm silicon wafer fabrication production line. [PERSON] et al. [16] developed a robust berth-scheduling algorithm to address the uncertainty of vessel arrival delay and handling time.