A solution algorithm for finding the best and the worst fuzzy compromise solutions of fuzzy rough linear programming problem with triangular fuzzy rough number coefficients

Temelcan, Gizem

doi:10.1007/s41066-022-00341-2

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…Sarwar et al [37] developed (a PROMETHEE) technique based on FRNs for selecting suppliers in industrial manufacturing. Upper and lower limits in the FRNs adequately characterize the ambiguity of the entire TFNs and the vagueness of any component containing the modal value, upper and lower bounds [43]. Hussain et al [19] proposed an intuitionistic fuzzy rough TOPSIS approach using Dombi operations.…”

Section: Authorsmentioning

confidence: 99%

Ranking Failure Modes with Pythagorean Fuzzy Rough VIKOR: A Case Study in Automatic Washing Machines

Shahzadi,

Chohan,

Zafar

2024

Preprint

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Failure mode and effects analysis (FMEA) is known for its remarkable and exceptional ability to recognize , evaluate, and remove possible failure modes in various industrial applications. It offers a broad point of view for scrutinizing possible failures, their roots, and their consequences in processes, manufactured articles, and designs. The conventional FMEA method has faced significant criticism due to its difficulties in accurately determining the weight of risk factors, recognizing the priority of failure modes, and handling vagueness during risk assessment. A new linguistic assessment model called Pythagorean fuzzy rough VIKOR is introduced to address these shortcomings. This model utilizes a combination of Pythagorean fuzzy numbers and rough information to improve the accuracy and effectiveness of risk assessment. We also apply the suggested VIKOR method to identify the less or more severe problems. Before applying the VIKOR method, we find criteria weights using the Pythagorean fuzzy rough entropy method. The importance of our proposed method has been discussed in the case study of an automatic washing machine. Different comparison studies show that the Pythagorean fuzzy rough method effectively addresses subjectivity and uncertainty when evaluating failure modes. This paper provides evidence to support using the Pythagorean fuzzy rough VIKOR (PFR-VIKOR) technique for ranking failure modes. This approach has demonstrated exceptional benefits in dealing with subjective and uncertain factors during the evaluation process.

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Section: Authorsmentioning

confidence: 99%

Ranking Failure Modes with Pythagorean Fuzzy Rough VIKOR: A Case Study in Automatic Washing Machines

Shahzadi,

Chohan,

Zafar

2024

Preprint

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“…(i) (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18; 0.2, 0.4, 0.6, 0.8) and B = (-1,0,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15; 0.2, 0.4, 0.6, 0.8)…”

Section: Numerical Examplementioning

confidence: 99%

“…( x ≤  (4,6,8,12,13,14,15,16,17,18,19,20,21,22,24,28,32)  (4,5,6,7,9,10,11,13,15,16,17,18,19,20,23,28,34)…”

Section: Numerical Examplementioning

confidence: 99%

“… (6,8,9,10,11,12,13,14,16,17,18,20,22,26,28,34,42) 2 x ≤  (8,9,10,14,15,17,18,19,20,22,24,26,27,28,29,34,37) Using the above proposed method, to get:  (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16, (6,8,9,10,11,12,13,14,16,17,18,20,22,26,28,34,42) =17.65  (8,9,10,14,15,17,18,19,20,22,24,26,27,28,29,34,37) = 20.9 The crisp linear programming problem is given by: = z Max .…”

Section: Numerical Examplementioning

confidence: 99%

“…Edrahimnejad et al (2022) discussed fuzzy linear programming problem based on trapezoidal fuzzy number. Temelcan (2023) proposed the triangular fuzzy rough number for fuzzy rough linear programming problem.…”

Section: Introductionmentioning

confidence: 99%

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Heptadecagonal Fuzzy Number and its Distinctive Representation, Ranking, Defuzzification Technique and Application in Fuzzy Linear Programming Problem

Bala,

Kumar,

Kadyan

2023

Preprint

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In this study, distinct categories of linear and nonlinear heptadecagonal fuzzy numbers with symmetry and asymmetry and their graphical representation are proposed. The membership functions and alpha-cuts for different types of heptadecagonal fuzzy numbers are also explained. Linear and nonlinear heptadecagonal fuzzy numbers with symmetry and asymmetry serve as powerful tools for simplifying the comprehension of vagueness data across different problems. By using the alpha-cut, the arithmetic operations on linear heptadecagonal fuzzy number with symmetry are addressed. A ranking method based on the centroid formula is used to rank two heptadecagonal fuzzy numbers and the alpha-cut method is applied to compute the defuzzification for linear heptadecagonal fuzzy number with symmetry. A numerical example for the fuzzy linear programming problem has been presented to illustrate the proposed methodology.

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Continuous triangular fuzzy generalized OWA operator and its application to combined prediction

Hu,

Zhou,

Zhang

et al. 2023

Soft Comput

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A solution algorithm for finding the best and the worst fuzzy compromise solutions of fuzzy rough linear programming problem with triangular fuzzy rough number coefficients

Cited by 8 publications

References 40 publications

Ranking Failure Modes with Pythagorean Fuzzy Rough VIKOR: A Case Study in Automatic Washing Machines

Ranking Failure Modes with Pythagorean Fuzzy Rough VIKOR: A Case Study in Automatic Washing Machines

Heptadecagonal Fuzzy Number and its Distinctive Representation, Ranking, Defuzzification Technique and Application in Fuzzy Linear Programming Problem

Continuous triangular fuzzy generalized OWA operator and its application to combined prediction

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