## 5 Conclusions and Future Work  
### Concluding Remarks  
The MCDM problems involving dependence and feedback effects are challenging for decision-makers to make the correct decisions. This challenge primarily arises due to the non-linear and interconnected nature of such systems. To address these issues, decision-makers often rely on advanced decision support systems capable of modeling and simulating various situations, including system dynamics, and employing optimization approaches that consider multiple criteria and their interactions. Within this context, cognitive maps have been widely used in policy analysis and decision-making support [80,81]. NCMs offer a potential framework for capturing and analyzing complex, uncertain, and conflicting information, making them a useful tool \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Criteria** & **Proposed Method-NCM** & **Fuzzy ANP** & **Fuzzy DEA** \\ \hline Ranking of alternatives & Yes & Yes & Yes \\ \hline Handling of indeterminacy & Yes & No & No \\ \hline Influence of one factor over other factors & Yes & No & No \\ \hline Dynamic behavior & Yes & No & No \\ \hline Stakeholder integration & Yes & Limited & Limited \\ \hline Flexibility of application & Yes & Limited & Limited \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison table of proposed model with existing FCM-based MCDM models. for decision-making and problem-solving in ambiguous and imprecise circumstances. Their ability to capture vague information and incorporate uncertainty provides decision-makers with a more comprehensive understanding of the systems they are dealing with, ultimately leading to better-informed decisions. The use of NCMs in decision-making has been studied in various fields, such as environmental management and corporate strategy [82]. Additionally, NCMs have been applied in project portfolio management to analyze interdependencies and evaluate risks through the use of neutrosophic logic and NCMs. This approach highlights the advantages of managing uncertainty and facilitating expert knowledge elicitation [83]. This article offers an overview of the NCM structure and operation, along with an illustrative example of its use in a decision-support application. Additionally, it introduces group NCMs and a framework for supporting group decision-making in the context of a smart port's performance evaluation under a neutrosophic environment. This approach could be considered as a valuable asset in the managerial toolbox, as group NCMs facilitate collaboration between multiple stakeholders or decision-makers. Each member can contribute their unique perspectives and expertise on the interrelationships and dynamics of the smart port's various components. The methodology's adaptability ensures it can be tailored to specific ports, addressing unique operational, technological, and ecological challenges. To the best of our knowledge, analysis of the influence of one dimension/factor over others, that is, whether they positively or negatively influence each other, is not conducted in related literature. To analyze the interconnections between the smart port performance dimensions, we have proposed a new framework that utilizes NCMs. More specifically, in this paper, we utilized an innovative method with NCMs to add a new dimension to cognitive maps by incorporating dynamic feedback analysis and analyzing interconnections between various critical dimensions that affect the evaluation of a smart port's performance. While the theory behind NCMs is well developed [29], their application to decision support is limited. The proposed model uses NCMs because neutrosophic logic can handle uncertainties related to experts' opinions on vagueness, ambiguity, and inconsistency. Additionally, NCMs allow for the creation of higher-level knowledge based on system applications, addressing the need to handle uncertainty and inaccuracy associated with real-world problems.