Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
A fuzzy approach to select the location of the distribution center
Fuzzy Sets and Systems
Generalizing TOPSIS for fuzzy multiple-criteria group decision-making
Computers & Mathematics with Applications
Fuzzy hierarchical TOPSIS for supplier selection
Applied Soft Computing
Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets
Applied Soft Computing
Using the Hamming distance to extend TOPSIS in a fuzzy environment
Journal of Computational and Applied Mathematics
Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites
Expert Systems with Applications: An International Journal
Enhancement of TOPSIS using compound linguistic ordinal scale and cognitive pairwise comparison
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management
Fuzzy Optimization and Decision Making
Membership maximization prioritization methods for fuzzy analytic hierarchy process
Fuzzy Optimization and Decision Making
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Group decision making is the process to explore the best choice among the screened alternatives under predefined criteria with corresponding weights from assessment of a group of decision makers. The Fuzzy TOPSIS taking an evaluated fuzzy decision matrix as input is a popular tool to analyze the ideal alternative. This research, however, finds that the classical fuzzy TOPSIS produces a misleading result due to some inappropriate definitions, and proposes the rectified fuzzy TOPSIS addressing two technical problems. As the decision accuracy also depends on the evaluation quality of the fuzzy decision matrix comprising rating scores and weights, this research applies compound linguistic ordinal scale as the fuzzy rating scale for expert judgments, and cognitive pairwise comparison for determining the fuzzy weights. The numerical case of a robot selection problem demonstrates the hybrid approach leading to the much reliable result for decision making, comparing with the conventional fuzzy Analytic Hierarchy Process and TOPSIS.