# Prediction of Ship-Unloading Time Using Neural Networks
## 2 Literature Review
A constructive heuristic was developed for the MIP model, resulting in a solution gap of 10.27% when compared to CPLEX. The solution time for the heuristic was at the second level, while CPLEX required 30 to 40 h to find a solution. [PERSON] et al. [33] investigated a stockyard storage space-allocation problem at a large iron ore terminal. They formulated a mixed-integer linear programming model aimed at minimizing the total travel distance for all incoming iron ores. The primary constraints included rules governing space allocation and the operations of stacker-reclaimers. A genetic algorithm-based heuristic was employed to solve the model. For smaller problems, the heuristic can achieve optimal solutions in seconds; however, for larger-scale problems, CPLEX is unable to find an optimal solution within 2 h, while the heuristic can provide near-optimal solutions in seconds. [PERSON] and [PERSON] [34] addressed a storage space-allocation problem in an iron-steel stockyard by developing a nonlinear programming model. The objective was to minimize transportation and penalty costs, with constraints related to geometric factors (length, width, and height) and differences in material types for spatial allocation. This problem was solved using an improved tabu search algorithm. [PERSON] et al. [35] examined a raw materials storage-allocation problem for a large-scale steelworks plant. They proposed a mixed-integer linear programming model, which was solved using CPLEX 9.02. The model's constraints included maintaining a safety distance between two stockpiles and ensuring materials balance. Their method enhanced current production practices. Neural networks in prediction: Various neural network variants have been employed for prediction and classification tasks. [PERSON] and [PERSON] [36] proposed a probabilistic neural network for monitoring manufacturing processes. This network featured Gaussian-mixture distributed parameters, which enhanced computational efficiency. [PERSON] et al. [37] developed a long short-term memory (LSTM) neural network for time-series prediction, utilizing partial least squares (PLS) to simplify the network architecture. The streamlined LSTM model effectively balanced strong generalization capabilities with a compact structure. [PERSON] et al. [12] employed a Backpropagation Neural Network (BP) combined with rough set theory for power load forecasting, using rough sets for dimensionality reduction. This approach yielded improved prediction results. [PERSON] and [PERSON] [13] introduced a probabilistic neural network for predicting earthquake magnitudes. [PERSON] [38] proposed an ensemble model that integrates time-series clustering techniques with deep learning methods for wind speed forecasting. The model utilized a Dirichlet mixture model and dynamic time warping to cluster features from the time-series data. The results indicated that this feature-clustering approach is a promising framework for forecasting. [PERSON] et al. [39] developed a machine learning method that combines the random vector functional link (RVFL) network with a moth search algorithm to predict missing values of total algal counts during water quality monitoring. The moth search algorithm optimized the input features for the RVFL network, resulting in predicted algal values that closely matched real observations. [PERSON] et al. [40] proposed four machine learning algorithms: RVFL, support vector machine, social media optimization, and k-nearest neighbors, to assess the impact of air injection and transverse baffles on the thermohydraulic performance of shell and tube heat exchangers. The results demonstrated that the RVFL model effectively identified nonlinear relationships between operating conditions and process responses. [PERSON] and [PERSON] [41] introduced an SCN model for predicting component concentrations in sodium aluminate liquor. This mechanistic model was integrated to elucidate the relationships between the two stockpiles and the two stockpiles.