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.

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 lack of ability to handle vagueness in the decision making practice has been main drawback of the conventional TOPSIS. Thus, type 1, type 2 and Z fuzzy sets have been applied with conventional TOPSIS to allow experts to incorporate imperfect information in analysis. However the existing methods do not take into account the influence degree of decision makers. Hence, a novel modification of TOPSIS method to handle vagueness and imperfect information in decision making practice is presented. The concept of Z- numbers is used to present decision maker's reliability. Furthermore, a hybrid analysis of decision making process that requires the use of human sensitivity to reflect influence degree of decision maker can be often expressed by a fuzzy rule base. The ranking based on proposed method is validated comparatively using Spearmen rho correlation coefficient. The result shows proposed method outperforms the existing non rule based version of TOPSIS in terms of ranking performance.