Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems

Gitinavard, Hossein; Mousavi, Seyed Meysam; Vahdani, Behnam

doi:10.1007/s00500-015-2006-9

Cited by 52 publications

(26 citation statements)

References 41 publications

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“…Chatterjee and Bose (2012), Nguyen et al (2015) and also Akhavan et al (2015) studied different MCDM problems using COPRAS-F method. COPRAS method has also been handled by new extensions of fuzzy sets such as intuitionistic or hesitant fuzzy sets (Razavi Hajiagha et al 2013;Bausys et al 2015;Gitinavard et al 2016). …”

Section: Literature Review On Copras-g Methodsmentioning

confidence: 99%

Fuzzy Copras Method for Performance Measurement in Total Productive Maintenance: A Comparative Analysis

Bekar

Çakmakçı

Kahraman

2016

Journal of Business Economics and Management

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Abstract. Modern manufacturing firms should be supported by effective maintenance to become successful in their operations. One of the approaches for improving the performance of maintenance activities is to implement a total productive maintenance (TPM) strategy. Overall equipment effectiveness (OEE) is the key measure of TPM. According to the results of the literature review, the performance elements measured by the OEE tool are not sufficient to describe the effectiveness of TPM implementation. Hence, we aim at developing and evaluating new performance measures oriented towards the quantification of TPM implementation effectiveness under fuzzy environment. For the evaluation of each performance measure, at first, the nominal group technique has been used. Then to determine whether these performance measures are statistically significant, conjoint analysis based experimental design has been applied. In the second step, COmplex PRoportional ASsessment of alternatives with Grey relations (COPRAS-G) and the fuzzy COPRAS method has been developed to evaluate these performance measures in TPM. Proposed fuzzy COPRAS method gives the reassuring results of ranking newly developed performance measures in TPM.Keywords: COPRAS-G, performance measurement, new performance measures, total productive maintenance, conjoint analysis, fuzzy COPRAS. IntroductionTPM is a new concept for maintenance that better optimizes the equipment effectiveness, minimizes breakdowns and encourages operators to autonomous maintenance for day-to-day activities involving total workforce (Andersson 2015). TPM aims to improve equipment effectiveness during the lifetime of the equipment. 664E. Turanoglu Bekar et al. Fuzzy COPRAS method for performance measurement in total productive ... Nakajima (1988) initiated TPM concept in the 1980s, which brought measurable metric named OEE for measuring productivity of individual equipment in a factory. It explains and measures losses of significant sides of manufacturing specifically availability, performance, and quality rate.OEE approach has been starting to be widely used as an important quantitative metric for measurement of productivity in manufacturing operations (Huang et al. 2003). The use of OEE varies from one industry to another, and it is tailored to fit to comply with industries' specific requirements.According to the literature review on performance evaluation in TPM, OEE metric has widely been used as an important performance measure, but it is not adequate to define the effectiveness of TPM. Jeon et al. (2011) also suggested measuring the performance of TPM in terms of efficiency. This has caused to a requirement for a thoroughly described performance measurement system for TPM which is capable of considering different significant elements of productivity in a manufacturing process. Therefore, in this study new performance measures having an impact on TPM are proposed and proposed performance measures are evaluated under fuzzy environment.The innovative side of this study is to develop new per...

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Section: Literature Review On Copras-g Methodsmentioning

confidence: 99%

Fuzzy Copras Method for Performance Measurement in Total Productive Maintenance: A Comparative Analysis

Bekar

Çakmakçı

Kahraman

2016

Journal of Business Economics and Management

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“…Over the past few years, it has been successfully applied to a vast range of decision making problems [40][41][42] . Assumed that the performance ratings of m alternatives with respect to n criteria are represented as a decision matrix …”

Section: The Copras Methodsmentioning

confidence: 99%

Failure Mode and Effect Analysis using Soft Set Theory and COPRAS Method

Wang¹,

You²,

Liu³

et al. 2017

IJCIS

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Failure mode and effect analysis (FMEA) is a risk management technique frequently applied to enhance the system performance and safety. In recent years, many researchers have shown an intense interest in improving FMEA due to inherent weaknesses associated with the classical risk priority number (RPN) method. In this study, we develop a new risk ranking model for FMEA based on soft set theory and COPRAS method, which can deal with the limitations and enhance the performance of the conventional FMEA. First, trapezoidal fuzzy soft set is adopted to manage FMEA team members' linguistic assessments on failure modes. Then, a modified COPRAS method is utilized for determining the ranking order of the failure modes recognized in FMEA. Especially, we treat the risk factors as interdependent and employ the Choquet integral to obtain the aggregate risk of failures in the new FMEA approach. Finally, a practical FMEA problem is analyzed via the proposed approach to demonstrate its applicability and effectiveness. The result shows that the FMEA model developed in this study outperforms the traditional RPN method and provides a more reasonable risk assessment of failure modes.

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“…The applications of MCDM have been widely discussed in many fields, such as supplier selection [4], [5], fault diagnosis [6], [7], and heartcare management [8], [9]. To make the decision outcome reliable, some techniques are applied to its optimization processes, including analytical hierarchy process (AHP) [10]- [12], decision making trial and evaluation laboratory (DEMATEL) method [13], [14], and technique for order preference by similarity to an ideal solution (TOPSIS) method [15]- [17]. The estimation outcome is assessed by combining the experts' synthesized opinions, it is vital to consider the uncertain information in the learning process.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis

Liu

Xiao

Lin

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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Multicriteria decision analysis evaluates multiple conflicting criteria in decision making, but conflicting criteria are typical in evaluating options. As the existing ordering operations involved in multicriteria decision making cannot easily be implemented with intervals, we assume that scalar representative values with intervals can effectively avoid this issue. To deal with interval-valued criteria, we propose a generalized golden rule representative value approach, which involves the sigmoid function of backpropagation neural networks to tune parameters. Our approach considers the uncertainties and side effects of the interval variables to improve individual scalar representative values. Based on numerical examples, we address the effectiveness of the proposed approach, and we provide a specific application concerning multicriteria decision making with interval criteria satisfaction.

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Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems

Cited by 52 publications

References 41 publications

Fuzzy Copras Method for Performance Measurement in Total Productive Maintenance: A Comparative Analysis

Fuzzy Copras Method for Performance Measurement in Total Productive Maintenance: A Comparative Analysis

Failure Mode and Effect Analysis using Soft Set Theory and COPRAS Method

A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis

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