A Brief Review and Future Outline on Decision Making Using Fuzzy Soft Set

Das, Sujit; Malakar, Debashish; Kar, Samarjit; Pal, Tandra

doi:10.4018/ijfsa.2018040101

Cited by 13 publications

(4 citation statements)

References 86 publications

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“…A fuzzy game theory approach can successfully allocate human resources and tasks in an organisation [ 37 ]. Fuzzy logic formulates the mathematical approach to assign membership values and to quantify and interpret the different variables in the required form [ 38 ]. Many extensions of fuzzy set have been continuously used to solve complex real-life problems [ 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Nurse allocation in hospital: hybridization of linear regression, fuzzy set and game-theoretic approaches

2022

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Sufficient numbers of staff are required for any hospital for providing better services to patients with satisfactory treatment. In most hospitals, the patient's treatment and care depend on the availability of nurses. Less number of nurses are a reason for average care and treatment to patients, whereas more than the required number of nurses may cause wastage of expenditure and manpower. More importantly, an additional number of nurses may be assigned to some hospitals where a sufficient number of nurses are not available. Moreover, nurses are of different categories such as qualified nurses having experience in providing service to patients, qualified nurses without having experience, and nurses without qualification but have experience. The expenditure also depends on categories of nurses' appointments. To reach an equilibrium point in appointing nurses, we propose a hybridization model using linear regression, fuzzy set theory and game-theoretic approaches. Regression analysis is implemented for prediction based on different fuzzy membership values of independent variables. We implement two concepts of game theory, the Nash equilibrium and the perfect Nash equilibrium. Implementing the Nash equilibrium, different equilibria values are generated and by implementing perfect Nash equilibrium, a subgame is generated to reach one equilibrium value. We have illustrated the proposed approach using a case study in which the linear regression approach is implemented to predict the patients’ arrival rate based on the monetary standard of the patient, communication facilities, patients curing chances, and patient choice towards the hospital, where these four features are quantified using fuzzy membership values. Nash equilibrium decides all possible ways of the nurses' allocations and perfect Nash equilibrium assist to reach an appropriate allocation. Thus, the hybridization of the regression approach, fuzzy membership and game theory approaches finalize the exact allocation of nurses. Finally, a comparative analysis is given to demonstrate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Nurse allocation in hospital: hybridization of linear regression, fuzzy set and game-theoretic approaches

2022

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“…After the introduction of fuzzy sets in 1965, the researchers modified the fuzzy set in various ways to manage the unexpected situations, which are unable to handle by the fuzzy sets. Some of the extension of fuzzy sets are fuzzy soft sets (Cagman et al 2011 ), intuitionistic fuzzy sets (Das et al 2018 ), inter value fuzzy sets (Gorzałczany, 1987 ), multi-fuzzy sets (Das et al 2013 ), fuzzy multi sets (Das et al 2013 ), picture fuzzy sets (Si et al 2019 ; Amanathulla et al 2021 ), and adaptive neuro-fuzzy (Cabalar et al 2012 ). The fuzzy set was generalized by Atanassov ( 1986 ) and converted into intuitionistic fuzzy sets (IFS).…”

Section: Introductionmentioning

confidence: 99%

Preferred hospitalization of COVID-19 patients using intuitionistic fuzzy set-based matching approach

Das

Kar

2022

Granul. Comput.

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Preferable hospitalization of COVID-19 patients has become an urgent and challenging task to save lives amidst the unexpected rising of the 3rd wave, where fuzzy set and matching techniques are considered due to their inherent capability to deal with uncertain suitable pair selection. The matching technique has been widely used to solve decision-making problems due to its capability to determine the suitable pair between the objects of two disjoint sets, whereas fuzzy set is well known to manage uncertain situations. This paper extends the matching technique using fuzzy set and proposes a novel fuzzy matching approach to solve uncertain decision-making problems. We also extend the fuzzy matching approach in the framework of an intuitionistic fuzzy set. A relation between the matching technique and fuzzy set theory is established by developing the preference sequence of the elements. The fuzzy entropy is used to measure the closeness among the elements between two distinct sets. Applicability of the proposed approach is measured by providing an illustrative case study concerned with the preferred hospitalization of the COVID-19 patients. Finally, a comparative study is given to analyze the effectiveness of the proposed approach, where the intuitionistic fuzzy set-based matching approach shows better performance compared to fuzzy and conventional matching based approach. For experimentation purpose, this study uses 9424 patients and 234 hospitals with a total available capacity of 18,024 beds.

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“…Therefore, we plan to review IVILDFS and its implementation which will help to fulfill this research gap, in decision-making model and explore further applicability of IVILDFS in real life decision-making issues. Now-a-days, fuzzy MAGDM [17][18][19][20][21][22][23][24][25][26] is a significant field of FS theory and has attracted numerous researchers. It is widely used in a variety of fields, like business, economy, science and technology, engineering fields, including management studies, project work, investment decision taking and so forth.…”

Section: Introductionmentioning

confidence: 99%

Interval-valued intuitionistic linear Diophantine fuzzy set and its applications in multi-attribute decision making problems

De¹,

Das

Kar

2022

Preprint

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The domain principle of intuitive fuzzy set (IFS), interval-value intuitionistic fuzzy set (IVIFS), Pythagorean fuzzy set (PFS), interval-valued Pythagorean fuzzy set (IVPFS) and q-rung orthopair fuzzy set (q-ROFS) have various real-life implementations in different fields. But researchers found some limitations to apply these concepts in much uncertain problems due to some issues related to membership and non-membership grads in above mention theories. In order to resolve the issues, firstly, we introduce the novel idea of the interval-valued intuitionistic linear Diophantine fuzzy set (IVILDFS) with the introduction of reference parameters. Next, we propose some score and accuracy functions for interval-valued intuitionistic linear Diophantine fuzzy numbers (IVILDFNs) and apply them in decision making paradigm. We also define interval-valued intuitionistic linear Diophantine fuzzy weighted geometric aggregation operator (IVILDFWGA) for decision making purpose. Then we present two algorithms to solve multiple attribute group decision making (MAGDM) problems using the propose score functions and IVILDFWGA operator. To illustrate the applicability of the proposed approach, we have used two numerical examples. Finally, we present a comparative study and sensitivity analysis to analyze the effectiveness of the proposed method with the existing approaches. The proposed study exhibits a useful way to solve fuzzy MAGDM problems in much efficient manner.

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A Brief Review and Future Outline on Decision Making Using Fuzzy Soft Set

Cited by 13 publications

References 86 publications

Nurse allocation in hospital: hybridization of linear regression, fuzzy set and game-theoretic approaches

Nurse allocation in hospital: hybridization of linear regression, fuzzy set and game-theoretic approaches

Preferred hospitalization of COVID-19 patients using intuitionistic fuzzy set-based matching approach

Interval-valued intuitionistic linear Diophantine fuzzy set and its applications in multi-attribute decision making problems

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