Abdul Malek Yaakob

Retirement adequacy is essential for maintaining the standard of living obtained during the working years. The purpose of this research is to apply the quantile Retirement adequacy is essential for maintaining the standard of living obtained during the working years. The purpose of this research is to apply the quantile

Energy-water efficiency forecasting is important to ensure a reliable supply of water for future generations as well as reduce energy consumption. Uncertainty,

Large-scale group decision-making (LSGDM) has become common in the new era of technology development involving a large number of experts. Recently, in the use of social network analysis (SNA), the community detection method has been highlighted by researchers as a useful method in handling the complexity of LSGDM. However, it is still challenging to deal with the reliability and hesitancy of information as well as the interpretability of the method. For this reason, we introduce a new approach of a Z-hesitant fuzzy network with the community detection method being put into practice for stock selection. The proposed approach was subsequently compared to an established approach in order to evaluate its applicability and efficacy.

Decision-making environment often encounters complexity along its processes, especially in the context of multidisciplinary scientific research. This can commonly be seen in engineering, computing, finance, astrology and other different areas. It is of great restriction in dealing with the practical problems which have diverse demands and properties. There is a growing body of literature that recognizes the importance of dealing with the complexity in decision making environment. The reliability and the transparency are the dominant feature of the integration of fuzzy network and Z-numbers. However, much of the research up to now has been descriptive in nature of the features. Hence, this proposed method is unique and novel because it offers some interesting insight of dealing with reliability and transparency of information in Z-hesitant fuzzy network decision-making environment. The fuzzy networks have the functionality under rule bases of fuzzy systems where it is recognized by its transparency and precision. The proposed method makes use of fuzzy network with the incorporation of hesitant fuzzy sets to assimilate decision information towards alternatives. For the validation and applicability purposes of the proposed method, the case study of stock evaluation assessed by a number of decision makers has been utilized as a real-world problem. The performance of the proposed method is evaluated respectively by applying the Spearman’s rho correlation. The result shows that the proposed method performs as the established method with the consideration of additional dominant features.

This paper introduces a novel modification of the technique for ordering of preference by similarity to ideal solution (TOPSIS) method and uses a fuzzy system with multiple rule bases to solve multi-criteria decision making problems where both benefit and cost criteria are presented as subsystems. Thus, the decision maker evaluates the performance of each alternative for optimization and further observes the performance for both benefit and cost criteria. This approach improves significantly the transparency of the TOPSIS method while ensuring high effectiveness in comparison to established methods. To ensure practicality and effectiveness of the proposed method, a traded equity case study is considered. Furthermore, the ranking based on the proposed method is validated comparatively using spearman rho correlation. The proposed method outperforms the existing TOPSIS methods in terms of ranking for the case study under consideration.

Fuzzy systems consisting of networked rule bases, called fuzzy networks, capture various types of imprecision inherent in financial data and in the decision-making processes on them. This paper introduces a novel extension of the technique for ordering of preference by similarity to ideal solution (TOPSIS) method and uses fuzzy networks to solve multicriteria decision-making problems where both benefit and cost criteria are presented as subsystems. Thus, the decision maker evaluates the performance of each alternative for portfolio optimization and further observes the performance for both benefit and cost criteria. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison with established approaches. The proposed method is further tested to solve the problem of selection/ranking of traded equity covering developed and emergent financial markets. The ranking produced by the method is validated using Spearman rho rank correlation. Based on the case study, the proposed method outperforms the existing TOPSIS approaches in terms of ranking performance.

The ability in providing result that is consistent with actual ranking remains the major concern in group decision making environment. The main aim of this paper is to introduce a novel modification of TOPSIS method to facilitate multi criteria decision making problems based on the concept of Z-numbers called Z-TOPSIS. The proposed method is adequate and intuitive in giving meaningful structure for formalizing information of a decision making problem, as it takes into account the decision makers' reliability. This study also provides bridge with some established knowledge in fuzzy sets to certain extend as to strengthen the concept of ranking alternatives using Znumbers. To ensure practicality and effectiveness of proposed method, stock selection problem is studied. The ranking based on proposed method is validated comparatively using spearman rho rank correlation. Based on the analysis, the proposed method outperforms the established TOPSIS methods in term of ranking performance.