ZBWM: The Z-number extension of Best Worst Method and its application for supplier development

• Published in: Expert systems with applications Vol. 107; pp. 115 - 125
• Main Authors: Aboutorab, Hamed, Saberi, Morteza, Asadabadi, Mehdi Rajabi, Hussain, Omar, Chang, Elizabeth
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.10.2018; Elsevier BV
• Subjects: Analytic hierarchy process; BWM; Data management; Decision making; Expert systems; Fuzzy sets; Fuzzy sets theory; Multiple criteria decision making; Set theory; Uncertainty; Unstructured data; Z-numbers; ZBWM; Z-numbers; BWM; Fuzzy sets theory; Unstructured data; ZBWM
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.04.015

•Proposing a novel integration of Z numbers and Best Worst Method.•The method results in lower inconsistency.•The uncertainty of the real word decisions is considered in the proposed method. Best Worst Method (BWM) has recently been proposed as a method for Multi Criteria Decision Making (MCDM). Studies show that BWM compared with other methods such as Analytic Hierarchy Process (AHP), leads to lower inconsistency of the results while reducing the number of required pairwise comparisons. MCDM methods such as BWM require accurate information. However, it often happens in practice that a level of uncertainty accompanies the information. The main aim of this paper is to address this problem and provide an integration of BWM and Z-numbers, namely ZBWM. Providing BWM with Z-numbers enables the BWM method to handle the uncertainty of information of a multi-criteria decision. Additionally, the capabilities of the proposed method in the process of utilizing the linguistic information dealing with big data are highlighted. The proposed method is examined to address a supplier development problem. By experimental results, we show that ZBWM results lower inconsistency when compared with BWM. A Z-number contains subjectivity in its fuzzy part, which can be addressed in future applications of ZBWM.