A multi‐criteria decision making approach for strategy formulation using Pythagorean fuzzy logic

Abstract

The objective of this paper is to develop Pythagorean fuzzy (PF) multi‐objective optimization by ratio analysis (PF‐MOORA) plus full MULTIplicative form (PF‐MULTIMOORA) method for solving multicriteria decision making (MCDM) problems with completely unknown information of criteria weights. In the model formulation process, a new distance measure is defined to quantify the difference between PF sets by combining Hamming distance and Hausdorff metric. This distance measure is, subsequently, implemented in entropy weight model for determining unknown weights of criteria, and also in reference point approach for obtaining preference indices of alternatives. To overcome the deficiencies occurred in existing MULTIMOORA method, like multiple comparisons, circular reasoning, and so on, an aggregation‐based approach is recommended in the proposed PF‐MULTIMOORA. To demonstrate the feasibility and practicality of the proposed method, an example concerning strategy prioritization of a tiles manufacturing company is presented. In the strategy evaluation process, at first, the judgement values provided by the decision maker are expressed in linguistic terms, and then those are converted into PF numbers through a PF weighting scale. The sensitivity of the proposed model is validated by changing of weights of criteria which impact on the ranks of the strategies. To show robustness of the developed method, the result attained by applying PF‐MULTIMOORA is compared with existing techniques, not only in crisp and fuzzy quantitative strategic planning matrix context, but also using four other MCDM methods, namely, modified PF‐MOORA, as a particular case of the proposed PF‐MULTIMOORA technique, PF weighted sum, PF‐TOPSIS and PF‐VIKOR.

Waste Clothing Recycling Channel Selection Using a CoCoSo-D Method Based on Sine Trigonometric Interaction Operational Laws with Pythagorean Fuzzy Information

“…In WASPAS [83], the PyFWA and PyFWG operators are used to aggregate the evaluation values of each attribute. Similarly, MUL-TIMOORA [84] also uses the PyFWA and PyFWG operators to calculate the ratio system model and full multiplicative form of each alternative. CoCoSo [78] also obtains the WSM and WPM models of the alternative through the PyFWA and PyFWG operators, and then calculates the three strategies and obtains the equilibrium compromise solution of the alternative, but P 3 is the best option.…”

“…(1) In the Pythagorean fuzzy MAGDM problem, except for the Entropy weighting model in Sarkar and Biswas [84] to calculate the objective weight of attributes, other group decision-making methods adopt the combination weight of subjective and objective. Among these combined weights, Rani et al [36,83] calculate the objective weight of attributes based on the E-D technique.…”

“…Existing Pythagorean fuzzy MADM techniques, such as PyF-TOPSIS [79], PyF-VIKOR [36], PyF-WASPAS [83], PyF-MULTIMOORA [84] and PyF-CoCoSo [78], are used to calculate the case in this paper to verify the effectiveness of our method. The calculations are listed in Table 12.…”

New entropy propositions for interval‐valued spherical fuzzy sets and their usage in an extension of ARAS (ARAS‐IVSFS)

“…It means the higher entropy the less important attribute. Based on this understanding, many authors decided to use entropy‐based objective weighting methods (Hafezalkotob & Hafezalkotob, 2015; Sarkar & Biswas, 2021; Shemshadi et al, 2011; Srdjevic et al, 2004; Wang & Lee, 2009).…”

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

“…Swarm intelligence optimization algorithms are a new evolutionary computing technology, which refers to some intelligent optimization algorithms with distributed intelligent behavior characteristics inspired by the swarm behavior of insects, herds, birds, fish, etc. [ 3 , 4 , 5 ]. This has become the research focus of more and more researchers.…”