An intuitionistic fuzzy entropy approach for supplier selection

Rahimi, Mohamadtaghi; Kumar, Pranesh; Moomivand, Behzad; Yari, Gholamhossein

doi:10.1007/s40747-020-00224-6

Cited by 36 publications

(18 citation statements)

References 47 publications

(72 reference statements)

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“…Szmidt and Kacprzyk ( 2001) firstly studied the definition of intuitionistic fuzzy (IF)-entropy measure. Further, several IF-entropy measures have been put forward with lots of applications (Bustince, H., Burillo, 1996;Vlachos and Sergiadis, 2007;Wei et al, 2012;Mishra 2016;Rani and Jain, 2017;Ansari et al, 2018;Mishra and Rani, 2019;Mishra et al, 2017aMishra et al, ,b, 2019aMishra et al, ,b, 2020aRahimi et al, 2021;Rani et al, 2021;Thao and Chou, 2022). To evade the drawback of some existing entropies, this section develops novel entropy for IFS and then apply it to develop a new feature selection method in the next section.…”

Section: Entropy For Ifssmentioning

confidence: 99%

Selecting features by utilizing intuitionistic fuzzy Entropy method

Pandey¹,

Mishra²,

Rani

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods.

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Section: Entropy For Ifssmentioning

confidence: 99%

Selecting features by utilizing intuitionistic fuzzy Entropy method

Pandey¹,

Mishra²,

Rani

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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show abstract

“…In the literature, it is seen that IFS is used in different decision making problems. Such as; resilient-green supplier selection problem (Xiong vd., 2020), selection of quality methods (Gojković et al, 2021), green supplier selection (Kumari and Mishra, 2020), coronavirus vaccine selection (Ecer, 2022), wastewater treatment plans selection (Zhou et al, 2018), sustainable supplier selection (Turk, 2022), lean six sigma project selection (Singh et al, 2021), healthcare waste treatment technologies selection (Salimian and Mousavi, 2022), site selection (Gao et al, 2021), supplier selection (Nakiboglu and Bulgurcu, 2020;Rahimi et al, 2021), IT personnel selection (Mishra et al, 2020), call center performance measurement (Oztaysi et al, 2020). Intuitionistic fuzzy set can be written as Eq (1) (Boran et al, 2009):…”

Section: Intuitionistic Fuzzy Setsmentioning

confidence: 99%

Human Resources Manager Selection Based on Fuzzy and Intuitionistic Fuzzy Numbers for Logistics Companies

Kara

Edinsel

Yalçın

2022

Mersin Üniversitesi Denizcilik Ve Lojistik Araştırmaları Dergisi

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Qualified managers are needed for the systematic functioning of logistics management applications. In logistics companies, the human resources manager takes part in determining the amount of personnel needed, supplying personnel, and creating personnel task forms. For successful human resources management, a qualified human resources manager should be selected. In the literature, it is seen that the manager selection problem is handled with multi criteria decision making (MCDM) methods. The aim of this research is to determine the problem criteria of human resources manager selection for logistics companies and to apply them with hybrid MCDM methods. With the in-depth literature review, ten criteria were determined for the logistics company human resources manager selection problem. The intuitionistic fuzzy weighted averaging (IFWA) method was used to weight the criteria. The fuzzy multi attribute ideal-real comparative analysis (F-MAIRCA) method was applied for the ranking of the four candidate managers. The application was made on a logistics company operating in Turkey. Research methods are based on fuzzy and intuitionistic fuzzy numbers. As a result of the study, the most important criterion in the selection of logistics human resources manager was determined as the experience criterion. Among the four candidates, the first candidate was seen as the best manager candidate. Suggestions have been developed for logistics companies, human resources manager candidates and researchers based on the research outputs. In addition, with this research, the IFWA and F-MAIRCA hybrid method has been brought to the literature.

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“…It utilizes the volatility of data to reflect the importance of data, the greater the volatility, the higher the information content, so as to give greater objective weight to the data, it is a very useful objective weighting method. Various kinds of fuzzy entropy have been put forward one after another, intuitionistic fuzzy entropy measures (Rahimi et al, 2021), Pythagorean fuzzy entropy measures (Thao & Smarandache, 2019;T. T. Xu et al, 2020), hesitant fuzzy entropy measure (Anees et al, 2020), dual hesitant fuzzy entropy measure (H. M. Zhang, 2020) in the corresponding fuzzy environment, DHF entropy measure (Hao et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Edas Method for Multiple Attribute Group Decision Making With Probabilistic Dual Hesitant Fuzzy Information and Its Application to Suppliers Selection

Ning

Lin

Wei

et al. 2023

Technological and Economic Development of Economy

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Probabilistic dual hesitant fuzzy set (PDHFS) is a more powerful and important tool to describe uncertain information regarded as generalization of hesitant fuzzy set (HFS) and dual HFS (DHFS), not only reflects the hesitant attitude of decision-makers (DMs), but also reflects the probability information of DMs. Score function of fuzzy number and weighting method are very important in multi-attribute group decision-making (MAGDM) issues. In many fuzzy environments, the score function and entropy measure have been proposed one after another. Firstly, based on the detailed analysis of the existed score function of PDHF element (PDHFE) and with the help of previous references, we build a novel score function for PDHFE. Secondly, a combined weighting method is built based on the minimum identification information principle by fusing PDHF entropy and Criteria Importance Through Intercriteria Correlation (CRITIC) method. Thirdly, a novel PDHF MAGDM approach (PDHF-EDAS) is built by extending evaluation based on distance from average solution (EDAS) approach to the PDHF environment to solve the issue that the decision attribute information is PDHFE. Finally, the practicability and effectiveness of the PDHF MAGDM technique is verified by suppliers selection (SS) and comparing analysis with existing methods.

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An intuitionistic fuzzy entropy approach for supplier selection

Cited by 36 publications

References 47 publications

Selecting features by utilizing intuitionistic fuzzy Entropy method

Selecting features by utilizing intuitionistic fuzzy Entropy method

Human Resources Manager Selection Based on Fuzzy and Intuitionistic Fuzzy Numbers for Logistics Companies

Edas Method for Multiple Attribute Group Decision Making With Probabilistic Dual Hesitant Fuzzy Information and Its Application to Suppliers Selection

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