# Prediction of Ship-Unloading Time Using Neural Networks
## 2 Literature Review  
There are several studies related to predicting unloading time, which can be summarized from three perspectives: ship-unloading scheduling, stockyard storage operations, and the application of neural networks for prediction. Ship-unloading scheduling: [PERSON] et al. [21] proposed a two-level stochastic model to estimate a container ship's handling time. The higher-level model employed a continuous-time Markov chain to estimate the handling time for loading and unloading, while the lower-level model utilized a closed queuing network to provide the transition matrix as input for the higher-level model. [PERSON] [22] addressed a container loading and unloading scheduling problem involving a set of ships, considering the storage location of each container, the assignment of vehicles to containers, and the scheduling of cranes for loading and unloading operations. The objective was to minimize the maximum time required to serve the set of ships. A heuristic approach was proposed to solve the problem, and the effectiveness of this heuristic was subsequently analyzed. [PERSON] et al. [23] introduced a stochastic mixed-integer programming model aimed at minimizing container ship handling time. A genetic algorithm was developed to construct the crane schedule while adhering to the operational rules governing crane usage. [PERSON] et al. [24] investigated a quay crane scheduling problem aimed at minimizing the completion time of loading and unloading operations for container ships. They formulated a mathematical programming model and proposed a Benders decomposition method to solve the problem. [PERSON] et al. [25] introduced a tabu search heuristic for quay crane scheduling, focusing on minimizing the completion time of loading and unloading operations for container ships. They considered specific operational constraints for the quay cranes, decomposing the problem into a routing problem and a scheduling problem, which were addressed using a tabu search heuristic and a local search technique, respectively. [PERSON] et al. [26] proposed a joint scheduling approach for quay cranes and trucks servicing a container ship. They developed a particle swarm optimization (PSO) method to tackle the scheduling problem. [PERSON] et al. [27] presented a knowledge-based approach for ship unloading, aimed at minimizing total demurrage costs. Their knowledge base was designed to manage the sequencing of waiting ships and the transfer of ships between two docks. [PERSON] et al. [28] proposed a heuristic approach for scheduling ship discharging. Their method considered the sequencing of holds and the assignment of ship unloaders, belt conveyor units, and stackers. [PERSON] and [PERSON] [29] introduced an integrated system for ship unloading, which coordinates two operations--dock assignment and ship discharging--to enhance the efficiency of unloading operations. [PERSON] and [PERSON] [30] developed a mixed-integer programming (MIP) model to address a berth-planning problem. In their approach, each ship was expected to remain for a fixed duration, irrespective of the berthing locations. A simulated annealing algorithm was devised to solve this problem. [PERSON] et al. [31] investigated a ship-unloadingissue for a large-scale steel plant, aiming to minimize the total flow time of all ships at the seaport. They developed a heuristic to address this challenge. [PERSON] et al. [1] examined the scheduling of ship unloading in a large-scale steel plant. They formulated a mathematical model and proposed a column-generation algorithm to solve the problem. Additionally, [PERSON] et al. [2] introduced a differential evolution algorithm for ship-unloading scheduling, with special attention given to the assignment of belt conveyors. Stockyard storage operations: [PERSON] et al. [32] proposed a mixed-integer programming (MIP) model for yard truck scheduling and storage allocation. Their objective was to minimize the weighted total cost, which included penalties for total delays and the costs associated with total travel time.