A novel multicriteria decision making (MCDM) approach for precise decision making under a fuzzy environment

The existing crisp and fuzzy multicriteria decision making (MCDM) methods exhibit consistency, complexity, and reliability issues. To address these challenges, we propose a new MCDM method called fuzzy technique for best-worst analysis (FTBWA). In FTBWA, a decision-maker (DM) first identifies a set of criteria and then determines the best-worst criteria. Next, the DM performs the fuzzy reference comparisons between the best-to-other (BtO) and the others-to-worst (OtW) criteria using the linguistic expressions. The process results in fuzzy BtO and fuzzy OtW vectors, which are then defuzzified to obtain quantifiable values. Afterward, a maximin problem is built and solved to obtain the weights of criteria and alternatives. The best alternative can be selected based on the final score obtained by aggregating the weights of different sets of criteria and alternatives. Further, we propose a consistency ratio to check the reliability of the results of FTBWA. To verify the practicality and consistency of FTBWA, we perform two illustrative case studies. Moreover, we perform a comprehensive analysis considering a comparative analysis, rank reversal analysis, and support for group decision making. From the results, we observe that FTBWA outperforms existing fuzzy/crisp MCDM methods.

Automated summary

This study introduces a new multicriteria decision making method, FTBWA, to address consistency, complexity, and reliability issues in existing methods. The researchers propose a consistency ratio to check the reliability of FTBWA results and conduct two case studies to verify practicality. Additionally, a comprehensive analysis including comparative analysis, rank reversal analysis, and support for group decision making shows that FTBWA outperforms existing fuzzy/crisp MCDM methods.