Resilient Supplier Selection Based on Fuzzy BWM and GMo-RTOPSIS under Supply Chain Environment

Resilient suppliers can reduce supply chain risk, effectively avoid supply chain disruption, and bring profits to enterprises. However, there is no united measuring index system to evaluate the resilient supplier under supply chain environment, and the assessment language sets are usually crisp values. Therefore, in order to fill the research gap, this paper proposes a hybrid method, which combines triangular fuzzy number, the best-worst method (BWM), and the modular TOPSIS in random environments for group decision-making (GMo-RTOPSIS) to solve the above problem. Firstly, the weight of decision-maker is calculated by using fuzzy BWM which can deal with triangular fuzzy numbers. Secondly, triangular fuzzy number is introduced into GMo-RTOPSIS, and combined with fuzzy BWM, alternatives are sorted to select the best resilient supply chain partner. Finally, the feasibility and universality of this method are proved by illustrative examples.

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New Similarity of Triangular Fuzzy Number and Its Application

The Scientific World JOURNAL ◽

10.1155/2014/215047 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Xixiang Zhang ◽

Weimin Ma ◽

Liping Chen

Keyword(s):

Collaborative Filtering ◽

Fuzzy Number ◽

Recommendation System ◽

Fuzzy Numbers ◽

Comprehensive Evaluation ◽

Cloud Model ◽

Triangular Fuzzy Number ◽

New Method ◽

Triangular Fuzzy Numbers

The similarity of triangular fuzzy numbers is an important metric for application of it. There exist several approaches to measure similarity of triangular fuzzy numbers. However, some of them are opt to be large. To make the similarity well distributed, a new method SIAM (Shape’s Indifferent Area and Midpoint) to measure triangular fuzzy number is put forward, which takes the shape’s indifferent area and midpoint of two triangular fuzzy numbers into consideration. Comparison with other similarity measurements shows the effectiveness of the proposed method. Then, it is applied to collaborative filtering recommendation to measure users’ similarity. A collaborative filtering case is used to illustrate users’ similarity based on cloud model and triangular fuzzy number; the result indicates that users’ similarity based on triangular fuzzy number can obtain better discrimination. Finally, a simulated collaborative filtering recommendation system is developed which uses cloud model and triangular fuzzy number to express users’ comprehensive evaluation on items, and result shows that the accuracy of collaborative filtering recommendation based on triangular fuzzy number is higher.

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Application of interval-valued triangular fuzzy numbers and their functional to the healthcare problems

Dependability ◽

10.21683/1729-2646-2021-21-1-23-33 ◽

2021 ◽

Vol 21 (1) ◽

pp. 23-33

Author(s):

Kapil Naithani ◽

Rajesh Dangwal

Keyword(s):

Failure Probability ◽

Fuzzy Number ◽

Fuzzy Numbers ◽

Fault Tree Analysis ◽

Triangular Fuzzy Number ◽

Triangular Fuzzy Numbers ◽

Index Method ◽

Estimate Reliability ◽

Failure Tree ◽

Interval Valued

Aim. In healthcare field there exist different types of uncertainty due to medical error generated by human and technologies. In general the crisp value generate loss of precision and inaccuracy about result and therefore the available data is not sufficient to assessed clinical process up to desired degree of accuracy. Therefore fuzzy set theory play as an important and advance role in accuracy of results in healthcare related problems. Methods. Here for more accuracy of result, we use functional fuzzy numbers in this paper. This study uses a new fuzzy fault tree analysis for patient safety risk modelling in healthcare. In this paper we will use level (λ, ρ) interval-valued triangular fuzzy number, their functional, t-norm operation and centre of gravity defuzzification method to evaluate fuzzy failure probability and estimate reliability of system. The effectiveness of these methods is illustrated by an example related to healthcare problems and then we analyse the result obtained with the other existing techniques. Tanaka et al.’s approach has been used to give the rank of basic events of the considered problems. Also, we use functional of fuzzy numbers to analyse the change in fuzzy failure probability. Results. The paper examines the application of the failure tree, t-norm and functional fuzzy numbers in the context of interval-valued triangular fuzzy numbers. The research examined two types of healthcare-specific problems and the corresponding defuzzification techniques for the purpose of reliability analysis using the existing methods. The authors concluded that t-norm is not associated with significant accumulation and identified how a functional fuzzy number affects reliability. Similarly, using the V index method, the least critical events were found for each system.

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Computing Cluster Centers of Triangular Fuzzy Numbers using Innovative Metric Distance

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j1140.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 3378-3381

Keyword(s):

Fuzzy Number ◽

Clustering Algorithm ◽

Fuzzy Numbers ◽

Triangular Fuzzy Number ◽

Triangular Fuzzy Numbers ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Metric Distance ◽

Matlab Programming ◽

Computing Cluster

In this paper we compute cluster centers of triangular fuzzy numbers through fuzzy c means clustering algorithm and kernel based fuzzy c means clustering algorithm. An innovative distance between the triangular fuzzy numbers is used and the distance is complete metric on triangular fuzzy numbers. The set of triangular fuzzy numbers and an another set with the same triangular fuzzy numbers by including an outlier or noisy point as an additional triangular fuzzy number are taken to find the cluster centers using MATLAB programming. An example is given to show the effectiveness between the algorithms.

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Grey Fuzzy Multiple Attribute Group Decision-Making Methods Based on Interval Grey Triangular Fuzzy Numbers Partitioned Bonferroni Mean

Symmetry ◽

10.3390/sym12040628 ◽

2020 ◽

Vol 12 (4) ◽

pp. 628 ◽

Cited By ~ 1

Author(s):

Kedong Yin ◽

Benshuo Yang ◽

Xue Jin

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Fuzzy Numbers ◽

Group Decision ◽

Triangular Fuzzy Number ◽

Triangular Fuzzy Numbers ◽

Bonferroni Mean ◽

Multiple Attribute ◽

Novel Method ◽

Symmetry Effect

Considering the characteristics such as fuzziness and greyness in real decision-making, the interval grey triangular fuzzy number is easy to express fuzzy and grey information simultaneously. And the partition Bonferroni mean (PBM) operator has the ability to calculate the interrelationship among the attributes. In this study, we combine the PBM operator into the interval grey triangular fuzzy numbers to increase the applicable scope of PBM operators. First of all, we introduced the definition, properties, expectation, and distance of the interval grey triangular fuzzy numbers, and then we proposed the interval grey triangular fuzzy numbers partitioned Bonferroni mean (IGTFPBM) and the interval grey triangular fuzzy numbers weighted partitioned Bonferroni mean (IGTFWPBM), the adjusting of parameters in the operator can bring symmetry effect to the evaluation results. After that, a novel method based on IGTFWPBM is developed for solving the grey fuzzy multiple attribute group decision-making (GFMAGDM) problems. Finally, we give an example to expound the practicability and superiority of this method.

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Group Decision Making Process for Supplier Selection with TOPSIS Method under Interval-Valued Intuitionistic Fuzzy Numbers

Advances in Fuzzy Systems ◽

10.1155/2012/407942 ◽

2012 ◽

Vol 2012 ◽

pp. 1-14 ◽

Cited By ~ 24

Author(s):

Mohammad Izadikhah

Keyword(s):

Decision Making ◽

Supply Chain ◽

Group Decision Making ◽

Supplier Selection ◽

Fuzzy Numbers ◽

Group Decision ◽

Topsis Method ◽

Intuitionistic Fuzzy Numbers ◽

Intuitionistic Fuzzy ◽

Interval Valued

Supplier selection is a fundamental issue of supply chain area that heavily contributes to the overall supply chain performance, and, also, it is a hard problem since supplier selection is typically a multicriteria group decision problem. In many practical situations, there usually exists incomplete and uncertain, and the decision makers cannot easily express their judgments on the candidates with exact and crisp values. Therefore, in this paper an extended technique for order preference by similarity to ideal solution (TOPSIS) method for group decision making with Atanassov's interval-valued intuitionistic fuzzy numbers is proposed to solve the supplier selection problem under incomplete and uncertain information environment. In other researches in this area, the weights of each decision maker and in many of them the weights of criteria are predetermined, but these weights have been calculated in this paper by using the decision matrix of each decision maker. Also, the normalized Hamming distance is proposed to calculate the distance between Atanassov's interval-valued intuitionistic fuzzy numbers. Finally, a numerical example for supplier selection is given to clarify the main results developed in this paper.

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A study on multiplication operation on triangular fuzzy numbers

Journal of Computational Mathematica ◽

10.26524/cm107 ◽

2021 ◽

Vol 5 (2) ◽

pp. 55-62

Author(s):

Mohamed Ali A ◽

Maanvizhi P

Keyword(s):

Fuzzy Number ◽

Fuzzy Numbers ◽

Triangular Fuzzy Number ◽

Fuzzy Arithmetic ◽

Triangular Fuzzy Numbers ◽

Arithmetic Operations ◽

Multiplication Operation ◽

Cut Method ◽

Standard Approximation ◽

Basic Content

The arithmetic operations on fuzzy number are basic content in fuzzy mathematics. But still the operations of fuzzy arithmetic operations are not established. There are some arithmetic operations for computing fuzzy number. Certain are analytical methods and further are approximation methods. In this paper we, compare the multiplication operation on triangular fuzzy number between α-cut method and standard approximation method and give some examples.

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Pythagorean Cubic fuzzy Hamacher aggregation operators and their application in green supply selection problem

AIMS Mathematics ◽

10.3934/math.2022263 ◽

2022 ◽

Vol 7 (3) ◽

pp. 4735-4766

Author(s):

Saleem Abdullah ◽

◽

Muhammad Qiyas ◽

Muhammad Naeem ◽

Mamona ◽

...

Keyword(s):

Decision Making ◽

Supply Chain ◽

Supply Chain Management ◽

Supplier Selection ◽

Fuzzy Numbers ◽

Group Decision ◽

Aggregation Operators ◽

Selection Problem ◽

Weighted Averaging ◽

Chain Management

<abstract><p>The green chain supplier selection process plays a major role in the environmental decision for the efficient and effective supply chain management. Therefore, the aim of this paper is to develop a mechanism for decision making on green chain supplier problem. First, we define the Hamacher operational law for Pythagorean cubic fuzzy numbers (PCFNs) and study their fundamental properties. Based on the Hamacher operation law of PCFNs, we defined Pythagorean cubic fuzzy aggregation operators by using Hamacher t-norm and t-conorm. Further, we develop a series of Pythagorean cubic fuzzy Hamacher weighted averaging (PCFHWA), Pythagorean cubic fuzzy Hamacher order weighted averaging (PCFHOWA) Pythagorean Cubic fuzzy Hamacher hybrid averaging (PCFHHA), Pythagorean Cubic fuzzy Hamacher weighted Geometric (PCFHWG), Pythagorean Cubic fuzzy Hamacher order weighted Geometric (PCFHOWG), and Pythagorean Cubic fuzzy Hamacher hybrid geometric (PCFHHA) operators. Furthermore, we apply these aggregation operators of Pythagorean Cubic fuzzy numbers to the decision making problem for green supplier selection. We construct an algorithm for the group decision making by using aggregation operators and score function. The proposed decision making method applies to green chain supplier selection problem and find the best green supplier for green supply chain management. The proposed method compared with other group decision techniques under Pythagorean cubic fuzzy information. From the comparison and sensitivity analysis, we concluded that our proposed method is more generalized and effective method.</p></abstract>

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A Zadeh′s max-min composition operator for 3-dimensional triangular fuzzy number

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-192095 ◽

2020 ◽

Vol 39 (3) ◽

pp. 3783-3793

Author(s):

Yong Sik Yun

Keyword(s):

Composition Operator ◽

Fuzzy Number ◽

Fuzzy Numbers ◽

Triangular Fuzzy Number ◽

Triangular Fuzzy Numbers ◽

3 Dimensional ◽

Definition Of

We generalized triangular fuzzy numbers from ℝ to ℝ 2 . By defining parametric operations between two α-cuts, which are regions, we obtained parametric operations for two triangular fuzzy numbers defined on ℝ 2 . We also generalized triangular fuzzy numbers from ℝ 2 to ℝ 3 . By defining parametric operations between two α-cuts, which are subsets of ℝ 3 , we derived parametric operations for two triangular fuzzy numbers defined on ℝ 3 . For the calculation of Zadeh’s principle operators, the definition of parametric operations between two α-cuts, which are subsets of ℝ 3 , is critical.

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AN EXTENSION OF THE RATIO SYSTEM APPROACH OF MOORA METHOD FOR GROUP DECISION-MAKING BASED ON INTERVAL-VALUED TRIANGULAR FUZZY NUMBERS

Technological and Economic Development of Economy ◽

10.3846/20294913.2015.1070771 ◽

2015 ◽

Vol 22 (1) ◽

pp. 122-141 ◽

Cited By ~ 17

Author(s):

Dragisa STANUJKIC

Keyword(s):

Decision Making ◽

Group Decision Making ◽

System Approach ◽

Fuzzy Numbers ◽

Group Decision ◽

Weighted Averaging ◽

Performance Ratings ◽

Triangular Fuzzy Numbers ◽

Moora Method ◽

Interval Valued

Decision-making in fuzzy environment is often a very complex, especially when related to predictions and assessments. The Ratio system approach of the MOORA method and Intervalvalued fuzzy numbers have already proved themselves as the effective tools for solving complex decision-making problems. Therefore, in this paper an extension of the Ratio system approach of the MOORA method, which allows a group decision-making as well as the use of interval-valued triangular fuzzy numbers, is proposed. Interval-fuzzy numbers are rather complex, and therefore, they are not practical for direct assigning performance ratings. For this reason, in this paper it has also been suggested the approach which allows the expression of individual performance ratings using crisp, interval or fuzzy numbers, and their further transformation into the group performance ratings, expressed in the form of interval-valued triangular fuzzy numbers, which provide greater flexibility and reality compared to the use of linguistic variables. Finally, in this paper the weighted averaging operator was proposed for defuzzification of interval-valued triangular fuzzy numbers.

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Pythagorean Fuzzy Hamy Mean Operators in Multiple Attribute Group Decision Making and Their Application to Supplier Selection

Symmetry ◽

10.3390/sym10100505 ◽

2018 ◽

Vol 10 (10) ◽

pp. 505 ◽

Cited By ~ 58

Author(s):

Zengxian Li ◽

Guiwu Wei ◽

Mao Lu

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Supplier Selection ◽

Fuzzy Numbers ◽

Group Decision ◽

Multiple Attribute ◽

Pythagorean Fuzzy Numbers

In this paper, we extend the Hamy mean (HM) operator and dual Hamy mean (DHM) operator with Pythagorean fuzzy numbers (PFNs) to propose Pythagorean fuzzy Hamy mean (PFHM) operator, weighted Pythagorean fuzzy Hamy mean (WPFHM) operator, Pythagorean fuzzy dual Hamy mean (PFDHM) operator, weighted Pythagorean fuzzy dual Hamy mean (WPFDHM) operator. Then the multiple attribute group decision making (MAGDM) methods are proposed with these operators. In the end, we utilize an applicable example for supplier selection to prove the proposed methods.

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