###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Geographical Zone** & **Methodology** & **Criteria** & **Review’s Papers** \\ \hline \multirow{4}{*}{1990-2000} & \multirow{4}{*}{North American, Asia} & \multirow{4}{*}{Surveys, Univariate and multivariate analysis} & Number of satilings, & \\  & & & Freight rates, User satisfaction, & \\  & & & & Loading and unloading Facilities, & \\  & & & & Equipment available, & \\  & & & & Port congestion, & \\  & & & Special handling ability & \\ \hline \multirow{4}{*}{2001-2010} & \multirow{4}{*}{Asia, Europe} & Discrete Choice Model-DCM, & \multirow{4}{*}{Port characteristics,} & \multirow{4}{*}{Number of berths,} \\  & & Analytic Hierarchy Process-AHP, & & Travel time, \\ \cline{1-1}  & & Gray Decision Model-GDM, & & Maritime costs, Reability, & \\ \cline{1-1}  & & & Surveys and ranking & Port charges and port services, & \\ \cline{1-1}  & & & of importance, & Number of routes and frequency, & \\ \cline{1-1}  & & Binary Logistic Regression-BLR & & Hinterland and quality of connection, & \\ \hline \multirow{4}{*}{2011–2020} & \multirow{4}{*}{Asia, Europe, South America} & \multirow{4}{*}{Discrete Choice Model-DCM, & \multirow{4}{*}{Port location, Port characteristics,} & \multirow{4}{*}{Number of berths,} \\  & & Analytic Hierarchy Process-AHP, & & \\ \cline{1-1}  & & Data Envelopment Analysis-DEA, & & \\ \cline{1-1}  & & Electric III, Promethe & & \\ \cline{1-1}  & & Stochastic Frontier Analysis–SFA, & & \\ \cline{1-1}  & & COPRAS–model & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{4}{*}{2021–2024} & \multirow{4}{*}{Asia, Europe} & Discrete Choice Model, & \multirow{4}{*}{Port location, Number of berths,} \\  & & Data Envelopment Analysis-DEA, & & \\ \cline{1-1}  & & Principal Component & & \\ \cline{1-1}  & & Analysis-PCA, Clustering & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Methodologies and criteria most used for the analysis and ranking of port terminals in recent years. Using Principal Component Analysis (PCA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the main aim of this study was to create a ranking of container terminals in LAC by implementing the innovative application of the discrete multi-attribute decision making (distance-based) TOPSIS model for assessing container terminals. The use of PCA aims to assess the dimensional reduction of variables to determine the significance of each variable within the ranking. And through TOPSIS, the ranking can be calculated and validated [17]. PCA is a classical technique commonly used for dimensionality reduction across a wide range of fields even in logistics and supply chain management strategies [18] or marine pollution. The synergy of methods: Combining PCA and TOPSIS leverages the strengths of both approaches. PCA reduces complexity and highlights key factors, while TOPSIS provides a detailed and weighted evaluation of each terminal [19]. Improved accuracy: Initially employing PCA to simplify and emphasize key factors makes the subsequent application of TOPSIS more precise and manageable [20]. Informed decision making: The combination of these methods establishes a solid foundation for decision making, ensuring that all relevant aspects are considered and subjectivity is minimized [21]. PCA reduces the dimensionality of data while retaining most of the original variability, facilitating the handling of complex data and avoiding redundancy [22]. Additionally, it enables the identification of key components that influence terminal performance, highlighting the most important factors for decision making. TOPSIS, as a multi-criteria evaluation method, considers multiple criteria simultaneously, providing a comprehensive evaluation of each container terminal. This method is based on proximity to the ideal solution, meaning terminals are evaluated based on their closeness to the best possible performance in each criterion [23]. In this research, the number of variables does not make dimensionality reduction essential.