# Predictive Analysis for Optimizing Port Operations
## 2 Literature Review  
Predictive Model Scheduling (PMS) is an important area of interest in port operations, focusing on the development of predictive models to optimize scheduling processes for port activities [12,13]. By leveraging predictive analytics for Total Time and Delay Time, PMS aims to enhance the efficiency and effectiveness of port operations by forecasting demand, optimizing resource allocation, and improving overall performance [14]. These papers have explored the application of machine learning and optimization algorithms for predictive scheduling in container terminals, demonstrating the potential of these methods in improving port productivity. Effective PMS relies on accurate estimates of vessel turnaround times and potential delays to make informed scheduling decisions. In this work, we extend these efforts by exploring more advanced methods, including deep learning, to achieve more accurate estimates for Total Time and Delay Time. These improved predictions can further enhance PMS by enabling better decision-making, optimizing scheduling, and ultimately improving the efficiency of port operations. The Berth Allocation Problem (BAP) is another critical challenge in port operations, aiming to optimize the allocation of berthing facilities for the incoming ships while considering various restrictions and objectives [2,15]. The estimation of different durations for the vessels is a crucial factor in BAP mathematical formulations, as it directly influences berth allocation decisions. Therefore, the development of efficient prediction and classification models for Total Time is strategically important for addressing the BAP. Studies by [PERSON] et al. and [PERSON] et al. [16; 17] have explored novel algorithms and optimization techniques to address the BAP, emphasizing the importance of accurate Total Time predictions in optimizing berth allocations. Performance management of ports and vessels is a critical aspect of maritime logistics, involving the monitoring, analysis, and optimization of Key Performance Indicators (KPIs) to ensure operational efficiency and competitiveness. [PERSON] et al. and [PERSON] et al. [18; 19] emphasize the importance of performance management systems for ports, highlighting the need for real-time data analytics and decision support tools to enhance operational performance. Regarding port and ship characteristics related to faults and accidents, the literature has focused on risk assessment and mitigation strategies to improve safety and reliability in port operations [20; 21]. In addition to the aforementioned areas, recent literature has also emphasized the importance of sustainability and green practices in port operations [11; 22]. With increasing global awareness of environmental issues, there has been a growing focus on developing sustainable and eco-friendly port operations. Sustainable port initiatives aim to minimize the environmental impact of port activities, reduce carbon emissions, and promote the use of renewable energy sources. Despite the significant work on port operations, there is an absence of studies on models for predicting and classifying the Total Time and Delay Time of vessels at ports, which highlights the need for research in this area [23; 24]. By addressing this gap, the proposed study aims to contribute insightful information for the decision-making processes in port management, ultimately improving the efficiency and effectiveness of port operations. This information will be crucial for solving other port-related problems, such as the BAP, as accurate predictions of Total Time and Delay Time can enhance the applicability of BAP solutions in real-world port environments. Moreover, these insights can support management decisions related to the planning, allocation, and scheduling of loading and unloading operations, contributing to overall operational excellence in port logistics. Prediction and classification both play important roles in this context.