Z-numbers based TOPSIS-similarity methodology for company performance assessment in Malaysia
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
The management of uncertainty in decision making problems is a very challenging research issues. The deterministic or probabilistic classical decision approaches quite often do not fit well to real world decision making problems. This proposal presents Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) using fuzzy similarity for z-numbers. The classical fuzzy TOPSIS techniques use closeness coefficient to determine the rank order by calculating Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) simultaneously. The authors propose fuzzy similarity to replace closeness coefficient by doing ranking evaluation. Fuzzy similarity is used to calculate the similarity between two fuzzy ratings (FPIS and FNIS). Fuzziness is not sufficient enough when dealing with real information and a degree of reliability of the information is very critical. Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in the uncertain information development to deal with linguistic decision making problems. A case study on group of company in Malaysia is given to illustrate the feasibility of the proposed methodology in ranking the performance assessment.
|Title of host publication||Applying Fuzzy Logic for the Digital Economy and Society|
|Editors||Andreas Meier, Edy Portmann, Luis Terán|
|Number of pages||17|
|Publication status||Published - 1 Mar 2019|
|Name||Fuzzy Management Methods|