Hybrid multi-criteria decision-making method with a bipolar fuzzy approach and its applications to economic condition analysis

Jana, Chiranjibe; Simic, Vladimir; Pal, Madhumangal; Sarkar, Biswajit; Pamucar, Dragan

doi:10.1016/j.engappai.2023.107837

Cited by 8 publications

(1 citation statement)

References 61 publications

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“…Soft set has found wideranging applications in various fields, including game theory, probability theory, smoothness of functions, Riemann integration, operations research, and Perron integration. Recently, researchers have shown significant interest in soft set and its various extensions [14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A progressive approach to multi-criteria group decision-making: N-bipolar hypersoft topology perspective

Musa,

Asaad

2024

PLoS ONE

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This paper investigates N-bipolar hypersoft topology (N-BHST), a novel extension of both the well-established N-hypersoft topology (N-HST) and hypersoft topology (HST). Deviating significantly from its precursor, the N-bipolar hypersoft (N-BHS) set, N-BHST introduces a multi-opinion approach to decision-making, augmenting robustness and adaptability. This innovative framework addresses identified limitations in N-bipolar soft topology (N-BST), especially in managing multi-argument approximate functions. The study analyzes various operators (closure, interior, exterior, and boundary) within the N-BHST framework, elucidating their interrelationships. Additionally, an examination is carried out on the enhancement of multi-criteria group decision-making (MCGDM) using N-BHST, setting it apart from existing models. A numerical example is presented to illustrate its application in real-world decision scenarios.

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Section: Introductionmentioning

confidence: 99%

A progressive approach to multi-criteria group decision-making: N-bipolar hypersoft topology perspective

Musa,

Asaad

2024

PLoS ONE

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TOPSIS Method Based on Intuitionistic Fuzzy Soft Set and Its Application to Diagnosis of Ovarian Cancer

Masmali,

Ahmad,

Azeem

et al. 2024

Int J Comput Intell Syst

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Fuzzy set theory is a mathematical method for dealing with uncertainty and imprecision in decision-making. Some of the challenges and complexities involved in medical diagnosis can be addressed with the help of fuzzy set theory. Ovarian cancer is a disease that affects the female reproductive system's ovaries, which also make the hormones progesterone and estrogen. The ovarian cancer stages demonstrate how far the disease has spread from the ovaries to other organs. The TOPSIS technique (Technique for Order Preference by Similarity to Ideal Solution) aids in selecting the best option from a selection of choices by taking into account a number of variables. It provides a ranking or preference order after weighing the benefits and drawbacks of each solution. Intuitionistic fuzzy soft set (IFSS) is the framework to deal with the uncertain information with the help of the parameters. The goal of this article is to develop some basic aggregation operators (AOs) based on the IFSS and then use them to diagnose the stages of the ovarian cancer using the TOPSIS technique. Furthermore, the variation of the parameters used in the developed model AOs is also observed and graphically represented.

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Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment

Zulqarnain,

Dai,

et al. 2024

Heliyon

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Hybrid multi-criteria decision-making method with a bipolar fuzzy approach and its applications to economic condition analysis

Cited by 8 publications

References 61 publications

A progressive approach to multi-criteria group decision-making: N-bipolar hypersoft topology perspective

A progressive approach to multi-criteria group decision-making: N-bipolar hypersoft topology perspective

TOPSIS Method Based on Intuitionistic Fuzzy Soft Set and Its Application to Diagnosis of Ovarian Cancer

Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment

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