Interval type-2 fuzzy TOPSIS approach with utility theory for subway station operational risk evaluation

2021 ◽  
Author(s):  
Zhenyu Zhang ◽  
Xuejun Zhao ◽  
Yong Qin ◽  
Hongyun Si ◽  
Lixin Zhou
Keyword(s):  
Risk Evaluation ◽  
Utility Theory ◽  
Operational Risk ◽  
Fuzzy Topsis ◽  
Subway Station ◽  
Topsis Approach ◽  
Interval Type

scholarly journals An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 182 ◽  
Author(s):  
Melih Yucesan ◽  
Suleyman Mete ◽  
Faruk Serin ◽  
Erkan Celik ◽  
Muhammet Gul
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Fuzzy Topsis ◽  
Future Research ◽  
Negative Effects ◽  
Green Supplier Selection ◽  
Managerial Implications ◽  
Interval Type

Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today’s organizations are seeking new ways to reduce the negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.

Risk assessment for the industrial network based on interval type-2 fuzzy sets

Kybernetes ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 916-937
Author(s):  
Chao Ren ◽  
Xiaoxing Liu ◽  
Zongqing Zhang
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Risk Evaluation ◽  
Quantitative Risk Assessment ◽  
Content Type ◽  
Industrial Network ◽  
Risk Magnitude ◽  
Interval Type

Purpose The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment. Design/methodology/approach This paper introduces an extended safety and critical effect analysis (SCEA) method, which takes the weight of each industry in a network into risk assessment. Furthermore, expert experience and fuzzy logic are introduced for the evaluation of other parameters. Findings The proposed approach not only develops weight as the fifth parameter in quantitative risk assessment but also applies the interval type-2 fuzzy sets to depict the uncertainty in the risk evaluation process. The risk rating of each parameter excluding weight is determined by using the interval type-2 fuzzy numbers. The risk magnitude of each industry in the network is quantified by the extended SCEA method. Research limitations/implications There is less study in quantitative risk assessment in the industrial network. Additionally, fuzzy logic and expert experience are expressed in the presented approach. Moreover, different parameters can be determined by different weights in network risk assessment in the future study. Originality/value The extended SCEA method presents a new way to measure risk magnitude for industrial networks. The industrial network is developed in risk quantification by assessing weights of nodes as a parameter into the extended SCEA. The interval type-2 fuzzy number is introduced to model the uncertainty of risk assessment and to express the risk evaluation information from experts.

SERVQUAL-Based Performance Analysis of Agricultural Financing in E-Banking Industry

2020 ◽  
pp. 21-41 ◽  
Author(s):  
Serhat Yüksel ◽  
Hasan Dinçer
Keyword(s):  
Commercial Banks ◽  
Banking Industry ◽  
Fuzzy Topsis ◽  
Fuzzy Dematel ◽  
Banking Services ◽  
Analysis Process ◽  
Qualified Personnel ◽  
Interval Type ◽  
Agricultural Regions

The aim of the study is to measure the effectiveness of commercial banks in the agricultural financing in Turkey. For this purpose, 10 different criteria are identified based on five different SERVQUAL perspectives. Moreover, 10 different Turkish deposit banks traded on BIST are taken into consideration in the analysis process. Interval type-2 fuzzy DEMATEL (IT2 FDEMATEL) method is used to weight the dimensions and criteria. Also, deposit banks are ranked with interval type-2 fuzzy TOPSIS (IT2 FTOPSIS). The findings show that flexibility of needs, branch availability, and qualified personnel are the most important criteria for agricultural financing. Hence, it is recommended that banks design a system in which customers can access the banks in flexible times related to the agricultural financing. Another important recommendation is that banks open new branches near the agricultural regions so that farmers can reach the banking services easily. Furthermore, banks should also improve their personnel for agricultural issues with necessary trainings.

Evaluation of Renewable Energy Alternatives using Hesitant Fuzzy TOPSIS and Interval Type-2 Fuzzy AHP

2015 ◽  
pp. 191-224 ◽  
Author(s):  
Başar Öztayşi ◽  
Cengiz Kahraman
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Fuzzy Sets ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Priority Weights ◽  
Interval Type

The selection among renewable energy alternatives is a fuzzy multicriteria problem with many conflicting criteria under uncertainty. In many decision-making problems, the Decision Makers (DM) define their preference in linguistic form since it is relatively difficult to provide exact numerical values during the evaluation of alternatives. Therefore, in many studies, fuzzy logic is successfully used to model this kind of uncertainty. In this chapter, the authors try to capture this uncertainty by using interval type-2 fuzzy sets and hesitant fuzzy sets. They propose a fuzzy multicriteria method for the evaluation of renewable energy alternatives, in which the priority weights of the criteria are determined by interval type-2 fuzzy AHP, and the alternatives are ranked using hesitant fuzzy TOPSIS. A case study is also given.

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