Robot selection problem via fuzzy TOPSIS method using novel distance and similarity measure for generalized fuzzy numbers with unequal heights

2021 ◽  
Author(s):  
Palash Dutta ◽  
Gourangajit Borah
Keyword(s):  
Similarity Measure ◽  
Fuzzy Numbers ◽  
Fuzzy Topsis ◽  
Selection Problem ◽  
Topsis Method ◽  
Generalized Fuzzy Numbers ◽  
Robot Selection

scholarly journals Extended Version of Linguistic Picture Fuzzy TOPSIS Method and Its Applications in Enterprise Resource Planning Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shouzhen Zeng ◽  
Muhammad Qiyas ◽  
Muhammad Arif ◽  
Tariq Mahmood
Keyword(s):  
Enterprise Resource Planning ◽  
Fuzzy Numbers ◽  
Distance Measure ◽  
Hamming Distance ◽  
Resource Planning ◽  
Fuzzy Topsis ◽  
Extended Version ◽  
Topsis Method ◽  
Planning Systems ◽  
Enterprise Resource Planning Systems

The main objective of the proposed research in this paper is introducing an extended version of the linguistic picture fuzzy TOPSIS technique and then solving the problems in enterprise resource planning systems. In this article, we use the uncertain information in terms of linguistic picture fuzzy numbers; the decision maker provides membership, neutral, and nonmembership fuzzy linguistic terms to represent uncertain assessments information of alternatives in linguistic multicriteria decision making (LMCDMs). In order to introduce the extended version of TOPSIS method, we defined a new hamming distance measure between two linguistic picture fuzzy numbers. Further, we apply the proposed method to problem of enterprise resource planning systems and discuss numerical implementation of the proposed method of LMCDM.

scholarly journals Refractured Well Selection for Multicriteria Group Decision Making by Integrating Fuzzy AHP with Fuzzy TOPSIS Based on Interval-Typed Fuzzy Numbers

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Tiejun Li ◽  
Jianhua Jin ◽  
Chunquan Li
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Numbers ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Selection Problem ◽  
Triangular Fuzzy Numbers ◽  
Analytic Hierarchy ◽  
Multicriteria Group Decision Making

Multicriteria group decision making (MCGDM) research has rapidly been developed and become a hot topic for solving complex decision problems. Because of incomplete or non-obtainable information, the refractured well-selection problem often exists in complex and vague conditions that the relative importance of the criteria and the impacts of the alternatives on these criteria are difficult to determine precisely. This paper presents a new model for MCGDM by integrating fuzzy analytic hierarchy process (AHP) with fuzzy TOPSIS based on interval-typed fuzzy numbers, to help group decision makers for well-selection during refracturing treatment. The fuzzy AHP is used to analyze the structure of the selection problem and to determine weights of the criteria with triangular fuzzy numbers, and fuzzy TOPSIS with interval-typed triangular fuzzy numbers is proposed to determine final ranking for all the alternatives. Furthermore, the algorithm allows finding the best alternatives. The feasibility of the proposed methodology is also demonstrated by the application of refractured well-selection problem and the method will provide a more effective decision-making tool for MCGDM problems.

scholarly journals An improvised similarity measure for generalized fuzzy numbers

2019 ◽  
Vol 8 (4) ◽  
pp. 1232-1238
Author(s):  
Daud Mohamad ◽  
Noorlisa Sara Adlene Ramlan ◽  
Sharifah Aniza Sayed Ahmad
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Similarity Measure ◽  
Fuzzy Numbers ◽  
Geometric Mean ◽  
Similarity Measures ◽  
Jaccard Index ◽  
Fuzzy Environment ◽  
Distance Methods ◽  
Generalized Fuzzy Numbers

Similarity measure between two fuzzy sets is an important tool for comparing various characteristics of the fuzzy sets. It is a preferred approach as compared to distance methods as the defuzzification process in obtaining the distance between fuzzy sets will incur loss of information. Many similarity measures have been introduced but most of them are not capable to discriminate certain type of fuzzy numbers. In this paper, an improvised similarity measure for generalized fuzzy numbers that incorporate several essential features is proposed. The features under consideration are geometric mean averaging, Hausdorff distance, distance between elements, distance between center of gravity and the Jaccard index. The new similarity measure is validated using some benchmark sample sets. The proposed similarity measure is found to be consistent with other existing methods with an advantage of able to solve some discriminant problems that other methods cannot. Analysis of the advantages of the improvised similarity measure is presented and discussed. The proposed similarity measure can be incorporated in decision making procedure with fuzzy environment for ranking purposes.

scholarly journals A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 204 ◽  
Author(s):  
Paweł Ziemba ◽  
Aneta Becker ◽  
Jarosław Becker
Keyword(s):  
Fuzzy Numbers ◽  
Fuzzy Topsis ◽  
Topsis Method ◽  
Aggregation Method ◽  
Average Value ◽  
Expert Opinions ◽  
Order Preference ◽  
Consensus Measure ◽  
Individual Expert ◽  
The Ideal

In the case of many complex, real-world decision problems solved with the participation of a group of experts, it is important to capture the uncertainty of opinions and preferences expressed. In such situations, one can use many modifications of the technique for order preference by similarity to the ideal solution (TOPSIS) method, for example, based on fuzzy numbers. In fuzzy TOPSIS, two aggregation methods of fuzzy expert opinions dominate, the first based on the average value technique and the second one extended by the minimum and maximum functions for determining the support of the aggregated fuzzy number. An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account. This article proposes the inclusion of the modified procedure for aggregating individual expert opinions, taking into account the degree of agreement of their opinions (called the similarity aggregation method—SAM) and the ranking of experts into the fuzzy TOPSIS method. The fuzzy TOPSIS method extended in this way was used to solve the decision problem of recruiting employees by a group of experts. As part of the solution, the modified SAM was compared with aggregation procedures based on the average value and min-max (minimum and maximum) support. The results of the conducted research indicate that SAM allows fuzzy numbers to be obtained, characterized by less imprecision and greater stability than the other two considered aggregation procedures.

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