A Z-number extension of the hybrid Analytic Hierarchy Process - Fuzzy Data Envelopment Analysis for risk assessment

Abstract

The hybrid Analytic Hierarchy Process-Fuzzy Data Envelopment Analysis is commonly used in solving Multiple Criteria Decision Making problems to cater for the uncertainty and subjectivity of human judgments. However, the use of Znumber in hybrid techniques is a new research area. Hence, the study on the impact and feasibility of Z-number extension has not been fully investigated. The main objective of this paper is to present an extension of Analytic Hierarchy Process-Fuzzy Data Envelopment Analysis method using Z-number. Two different multiplication relation operations were used to convert fuzzy numbers into Z-numbers by integrating the reliability component R into the restriction component à . The next step is to integrate the criteria weights into Z-Data Envelopment Analysis process. A numerical example is given to illustrate the application of the proposed Analytic Hierarchy Process-Z-Number DataEnvelopment Analysis methodology in ranking the risk priority of 20 bridge structures. The results showed that it is highly feasible to use the proposed hybrid Analytic Hierarchy Process-Fuzzy Data Envelopment Analysis in risk ranking. This information is very useful for improving characteristics of the alternatives understudied.

Risk Prioritization in Failure Mode and Effects Analysis with Extended SWARA and MOORA Methods Based on Z-Numbers Theory

“…Z-Number was first proposed by Zadeh (2011), as a general characteristic of the theory of uncertainty. The Z-Number in combination with AHP was used to identify reliable assessment criteria from the best universities in adverse environmental conditions (Sahrom and Dom, 2015), using the AHP-Fuzzy DEA and Z-Number method and integrating the concepts of reliability and fuzzy numbers, and worked on the priority assessment of 20 bridge structures. Also, Azadeh and Kokabi (2016) used a Z-DEA model to choose the IT project to deal with uncertainty, interaction between projects and trust.…”

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

“…Peng and Wang (2017) introduce hesitant uncertain linguistic Z-numbers using linguistic models and solve an enterprise resource planning problem for proof of the validity of the proposed method. Sahrom and Dom (2015) extent the AHP-Fuzzy Data Envelopment Analysis Method using Z-numbers through integrating reliability and fuzzy numbers and ranking the priority of 20 bridge structures. Yaakob and Gegov (2016) proposed a modification of TOPSIS method to solve MCDM problems based on the concept of Z-numbers (Z-TOPSIS) and solve a stock selection problem for demonstration.…”

“…The second component can be performed based on five linguistic variables. The rules of transformation are listed in Table 2 (Sahrom et al, 2015).…”

Semantic Risk Analysis Based on Single-Valued Neutrosophic Sets

“…A number of theories have been developed on fuzzy risk analysis, such as Bayes theory [5]- [7], Dempster-Shafer theory of evidence [8]- [13], fuzzy set theory [14]- [19], rough sets [20]- [22], intuitionistic fuzzy sets [23]- [25], D numbers [26] and Z numbers [27]- [29]. However, neutrosophic set is rarely used in fuzzy risk analysis.…”