A family of similarity measures for q‐rung orthopair fuzzy sets and their applications to multiple criteria decision making

2021 ◽  
Author(s):  
Bahram Farhadinia ◽  
Sohrab Effati ◽  
Francisco Chiclana
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Multiple Criteria Decision Making ◽  
Similarity Measures ◽  
Multiple Criteria

scholarly journals Multiple Criteria Decision-Making Based on Vector Similarity Measures under the Framework of Dual Hesitant Fuzzy Sets

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Juan Luis García Guirao ◽  
M. Sarwar Sindhu ◽  
Tabasam Rashid ◽  
Agha Kashif
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Multiple Criteria Decision Making ◽  
Similarity Measures ◽  
Smart Phone ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Hesitant Fuzzy Sets ◽  
Investment Companies ◽  
Selection Of

Similarity measures have a great importance in the decision-making process. In order to identify the similarity between the options, many experts have established several types of similarity measures on the basis of vectors and distances. The Cosine, Dice, and Jaccard are the vector similarity measures. The present work enclosed the modified Jaccard and Dice similarity measures. Founded on the Dice and Jaccard similarity measures, we offered a multiple criteria decision-making (MCDM) model under the dual hesitant fuzzy sets (DHFSs) situation, in which the appraised values of the alternatives with respect to criteria are articulated by dual hesitant fuzzy elements (DHFEs). Since the weights of the criteria have a much influence in making the decisions, therefore decision makers (DMs) allocate the weights to each criteria according to their knowledge. In the present work, we get rid of the doubt to allocate the weights to the criteria by taking an objective function under some constraints and then extended the linear programming (LP) technique to evaluate the weights of the criteria. The Dice and Jaccard weighted similarity measures are practiced amongst the ideal and each alternative to grade all the alternatives to get the best one. Eventually, two practical examples, about investment companies and selection of smart phone accessories are assumed to elaborate the efficiency of the proposed methodology.

scholarly journals Multiple Criteria Decision Making Based on Probabilistic Interval-Valued Hesitant Fuzzy Sets by Using LP Methodology

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
M. Sarwar Sindhu ◽  
Tabasam Rashid ◽  
Agha Kashif ◽  
Juan Luis García Guirao
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Real Life ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Occurrence Probability ◽  
Hesitant Fuzzy Sets ◽  
Life Problems ◽  
Interval Valued

Probabilistic interval-valued hesitant fuzzy sets (PIVHFSs) are an extension of interval-valued hesitant fuzzy sets (IVHFSs) in which each hesitant interval value is considered along with its occurrence probability. These assigned probabilities give more details about the level of agreeness or disagreeness. PIVHFSs describe the belonging degrees in the form of interval along with probabilities and thereby provide more information and can help the decision makers (DMs) to obtain precise, rational, and consistent decision consequences than IVHFSs, as the correspondence of unpredictability and inaccuracy broadly presents in real life problems due to which experts are confused to assign the weights to the criteria. In order to cope with this problem, we construct the linear programming (LP) methodology to find the exact values of the weights for the criteria. Furthermore these weights are employed in the aggregation operators of PIVHFSs recently developed. Finally, the LP methodology and the actions are then applied on a certain multiple criteria decision making (MCDM) problem and a comparative analysis is given at the end.

Multiple criteria decision making based on Hamy mean operators under the environment of spherical fuzzy sets

2021 ◽  
pp. 1-26
Author(s):  
Muhammad Sarwar Sindhu ◽  
Tabasam Rashid ◽  
Agha Kashif
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Closed Interval ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Vital Role ◽  
Multiple Criteria ◽  
Uncertain Information ◽  
Membership Degree ◽  
Picture Fuzzy Sets

Aggregation operators are widely applied to accumulate the vague and uncertain information in these days. Hamy mean (HM) operators play a vital role to accumulate the information. HM operators give us a more general and stretchy approach to develop the connections between the arguments. Spherical fuzzy sets (SpFSs), the further extension of picture fuzzy sets (PcFSs) that handle the data in which square sum of membership degree (MD), non-membership degree (NMD) and neutral degree (ND) always lie between closed interval [0, 1]. In the present article, we modify the HM operators like spherical fuzzy HM (SpFHM) operator and weighted spherical fuzzy HM (WSpFHM) operator to accumulate the spherical fuzzy (SpF) information. Moreover, various properties and some particular cases of SpFHM and the WSpFHM operators are discussed in details. Also, to compare the results obtained from the HM operators a score function is developed. Based on WSpFHM operator and score function, a model for multiple criteria decision-making (MCDM) is established to resolve the MCDM problem. To check the significance and robustness of the result, a comparative analysis and sensitivity analysis is also performed.

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