Process design and capability analysis using penthagorean fuzzy sets: surgical mask production machines comparison

2021 ◽  
pp. 1-13
Author(s):  
Elif Haktanır ◽  
Cengiz Kahraman
Keyword(s):  
Fuzzy Sets ◽  
Process Capability ◽  
Intuitionistic Fuzzy Sets ◽  
Process Capability Analysis ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Capability Indices ◽  
Capability Analysis ◽  
Tremendous Importance ◽  
Picture Fuzzy Sets

Process capability analysis (PCA) is a tool for measuring a process’s ability to meet specification limits (SLs), which the customers define. Process capability indices (PCIs) are used for establishing a relationship between SLs and the considered process’s ability to meet these limits as an index. PCA compares the output of a process with the SLs through these capability indices. If the customers’ needs contain vague or imprecise terms, the classical methods are inadequate to solve the problem. In such cases, the information can be processed by the fuzzy set theory. Recently, ordinary fuzzy sets have been extended to several new types of fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and spherical fuzzy sets. In this paper, a new extension of intuitionistic fuzzy sets, which is called penthagorean fuzzy sets, is proposed, and penthagorean fuzzy PCIs are developed. The design of production processes for COVID-19 has gained tremendous importance today. Surgical mask production and design have been chosen as the application area of the penthagorean fuzzy PCIs developed in this paper. PCA of the two machines used in surgical mask production has been handled under the penthagorean fuzzy environment.

Process capability analysis using intuitionistic fuzzy sets

2017 ◽  
Vol 32 (3) ◽  
pp. 1659-1671 ◽  
Author(s):  
Cengiz Kahraman ◽  
Abbas Parchami ◽  
Sezi Cevik Onar ◽  
Basar Oztaysi
Keyword(s):  
Fuzzy Sets ◽  
Process Capability ◽  
Intuitionistic Fuzzy Sets ◽  
Process Capability Analysis ◽  
Intuitionistic Fuzzy ◽  
Capability Analysis

New Einstein Hybrid Aggregation Operators for Intuitionistic Fuzzy Sets and Applications in Multi-Criteria Decision-Making

2019 ◽  
pp. 252-283
Author(s):  
Bhagawati Prasad Joshi ◽  
Abhay Kumar
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Decision Maker ◽  
Intuitionistic Fuzzy Sets ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Decision Making Processes

The fusion of multidimensional intuitionistic fuzzy information plays an important part in decision making processes under an intuitionistic fuzzy environment. In this chapter, it is observed that existing intuitionistic fuzzy Einstein hybrid aggregation operators do not follow the idempotency and boundedness. This leads to sometimes illogical and even absurd results to the decision maker. Hence, some new intuitionistic fuzzy Einstein hybrid aggregation operators such as the new intuitionistic fuzzy Einstein hybrid weighted averaging (IFEHWA) and the new intuitionistic fuzzy Einstein hybrid weighted geometric (IFEHWG) were developed. The new IFEHWA and IFEHWG operators can weigh the arguments as well as their ordered positions the same as the intuitionistic fuzzy Einstein hybrid aggregation operators do. Further, it is validated that the defined operators are idempotent, bounded, monotonic and commutative. Then, based on the developed approach, a multi-criteria decision-making (MCDM) procedure is given. Finally, a numerical example is conducted to demonstrate the proposed method effectively.

Multi-Attribute Group Decision Making Models under Intuitionistic Fuzzy Environment

2012 ◽  
Vol 263-266 ◽  
pp. 3225-3229
Author(s):  
Rong Duan ◽  
Qing Bang Han ◽  
Zuo Wang
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Information Entropy ◽  
Group Decision ◽  
Intuitionistic Fuzzy Sets ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Decision Making Models ◽  
Evaluation Matrix

In order to solve the problem of multi-attribute group-decision making with the elements of evaluation matrix are intuitionistic fuzzy sets, this paper offers corresponding TOPSIS models based on the information entropy weights and examples to be verified. The examples show the feasibility and effectiveness of the proposed models.

scholarly journals Process Capability Analysis Using Interval Type-2 Fuzzy Sets

2017 ◽  
Vol 10 (1) ◽  
pp. 721 ◽  
Author(s):  
Abbas Parchami ◽  
Sezi Çevik Onar ◽  
Baaşar Öztaysi ◽  
Cengiz Kahraman
Keyword(s):  
Fuzzy Sets ◽  
Process Capability ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
Interval Type

scholarly journals CAPABILITY INDICES FOR CONTROL CHARTS BASED ON REGRESSION MODELS

2015 ◽  
Vol 12 (2) ◽  
pp. 234
Author(s):  
Fernanda Siqueira Souza ◽  
Danilo Cuzzuol Pedrini ◽  
Carla Schwengber Ten Caten
Keyword(s):  
Quality Improvement ◽  
Control Charts ◽  
Regression Models ◽  
Process Capability ◽  
Process Capability Analysis ◽  
Capability Indices ◽  
Capability Analysis ◽  
Simulated Process

Process capability analysis is extremely important for optimization and quality improvement. It verifies whether the process under analysis is capable of producing items within engineering and customers’ specifications. The use of capability indices when assumptions are not satisfied leads to erroneous conclusions, compromising the study and analysis of the process, jeopardizing the fulfillment of requirements from management or external customers. Aiming at filling a gap identified in the literature, the main contributions of this work are: (i) proposition of capability indices for processes monitored through control charts based on regression models, for symmetric and asymmetric specifications; and (ii) comparison of the proposed indices with traditional capability indices through a simulated process.

scholarly journals Analisis Masalah Kualitas pada M/C Crank Shaft M2 dengan Menggunakan Tool Capability Process di PT XYZ, Pegangsaan Dua, Jakarta

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Gidion Karo Karo ◽  
Jessie Deborah R. Makapedua
Keyword(s):  
Process Improvement ◽  
Control Process ◽  
Process Capability ◽  
Repair Process ◽  
Process Capability Analysis ◽  
Normality Test ◽  
Quality Control Process ◽  
Capability Indices ◽  
Capability Analysis

<p>Process Capability is a tool that is often used in the process of quality improvement, especially for process improvement. This study uses a process capability analysis on crank shaft production line 2 for motorcycles. By using normality test data and process capability indices for calculation of Cp/Cpk, shows that most of the data obtained are not normally distributed, so need to transform the data into normal, which can then be followed by the calculation of process capability. For the calculation of Cp/Cpk, it was found that there were some machines that still need to get tight control to meet the specification. It shows that mass production is still less stable. In order to meet the specifications, it is necessary to improve the quality of the repair process to reduce the variation in the process.</p><p>Keywords: Process Capability, Quality Control, Process Improvement</p>

scholarly journals Analyzing of Process Capability Indices based on Neutrosophic Sets

2021 ◽  
Author(s):  
Selin Yalçın ◽  
Ihsan Kaya
Keyword(s):  
Automotive Industry ◽  
Process Capability ◽  
Process Capability Indices ◽  
Neutrosophic Sets ◽  
Modeling Uncertainty ◽  
Inaccurate Information ◽  
Process Capability Analysis ◽  
Capability Indices ◽  
Capability Analysis ◽  
Human Thinking

Abstract Process capability analysis (PCA) is an important statistical analysis approach for measuring and analyzing the ability of the process to meet specifications. This analysis has been applied by producing process capability indices (PCIs). \({C}_{p}\) and \({C}_{pk}\) are the most commonly used PCIs for this aim. Although they are completely effective statistics to analyze process’ capability, the complexity of the production processes based on uncertainty arising from human thinking, incomplete or vague information makes it difficult to analyze the process capability with precise values. When there is uncertain, complex, incomplete and inaccurate information, the capability of the process is successfully analyzed by using the fuzzy sets. Neutrosophic sets (NSs), one of the new fuzzy set extensions, have a significant role in modeling uncertainty, since they contain the membership functions of truth, indeterminacy, and falsity definitions rather than an only membership function. This feature provides a strong advantage for modeling uncertainty. In this paper, PCA has been performed based on NSs to overcome uncertainties of the process. For this purpose, specification limits (SLs) have been reconsidered by using NSs and two of the well-known process capability indices (PCIs) named \({C}_{p}\) and \({C}_{pk}\) have been reformulated. Finally, the neutrosophic process capability indices (NPCIs) named \({C}_{p}\) \(\left({\tilde{\stackrel{⃛}{C}}}_{p}\right)\) and \({C}_{pk}\) \(\left({\tilde{\stackrel{⃛}{C}}}_{pk}\right)\) have been derived for three cases that are created by defining SLs. Additionally, the obtained NPCIs have also been applied and confirmed on real case problems from automotive industry. The obtained results show that the NPCIs support the quality engineers to easily define SLs and obtain more flexible and realistic evaluations for PCA.

Novel Correlation Coefficient for Intuitionistic Fuzzy Sets and Its Application to Multi-Criteria Decision-Making Problems

2021 ◽  
Vol 10 (2) ◽  
pp. 39-58
Author(s):  
Ejegwa Paul Augustine
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Correlation Coefficient ◽  
Intuitionistic Fuzzy Sets ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Generalized Correlation

Correlation coefficient is an essential measuring operator in an intuitionistic fuzzy environment use in solving MCDM problems. In this paper, Xu et al.'s correlation coefficient for IFSs is generalized for an improved output. The objectives of this work are to generalize the triparametric correlation coefficient for IFSs proposed by Xu et al. and unravel its applicability in some MCDM problems. The generalized correlation coefficient for IFSs is characterized with some number of results. Some numerical illustrations are supplied to validate the preeminence of the generalized correlation coefficient for IFSs over some existing correlation coefficient measures. In addition, some MCDM problems such as determination of suitable lecturer for course allocation and personnel promotion exercise captured in intuitionistic fuzzy pairs are discussed with the aid of the proposed correlation coefficient.

SOME OPERATIONS OVER ATANASSOV'S INTUITIONISTIC FUZZY SETS BASED ON EINSTEIN T-NORM AND T-CONORM

2013 ◽  
Vol 21 (02) ◽  
pp. 263-276 ◽  
Author(s):  
WEIZE WANG ◽  
XINWANG LIU
Keyword(s):  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Scalar Multiplication ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Linguistic Hedges ◽  
Atanassov’S Intuitionistic Fuzzy Sets ◽  
Einstein Product

We define some operations over Atanassov's intuitionistic fuzzy sets (AIFSs), such as Einstein intersection, Einstein product, Einstein scalar multiplication and Einstein exponentiation, etc., and then define new concentration and dilation of AIFSs. These definitions will be useful while dealing with various linguistic hedges like “very”, “more or less”, “highly”, “very very” etc., involved in the problems under intuitionistic fuzzy environment. We also prove some propositions and present some examples in this context.

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