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Z-numbers based TOPSIS-similarity methodology for company performance assessment in Malaysia

Research output: Chapter in Book/Report/Conference proceedingChapter (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.
Original languageEnglish
Title of host publicationApplying Fuzzy Logic for the Digital Economy and Society
EditorsAndreas Meier, Edy Portmann, Luis Terán
PublisherSpringer
Pages97-113
Number of pages17
ISBN (Electronic)978-3-030-03368-2
ISBN (Print)978-3-030-03367-5
DOIs
Publication statusPublished - 1 Mar 2019

Publication series

NameFuzzy Management Methods
PublisherSpringer
ISSN (Print)2196-4130
ISSN (Electronic)2196-4149

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