Multi-criteria group decision making based on graph neural networks in Pythagorean fuzzy environment

Meng, Zhenhua; Lin, Rongheng; Wu, Budan

doi:10.1016/j.eswa.2023.122803

Cited by 10 publications

(1 citation statement)

References 45 publications

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“…One notable extension is the Pythagorean fuzzy set. Through incorporating degrees of membership, non-membership, and indeterminacy, this extension allows decision-makers to articulate their preferences with greater nuance (Meng et al 2024). The interval-valued Pythagorean fuzzy set takes a step beyond by assigning an interval to each element.…”

Section: Introductionmentioning

confidence: 99%

Location selection for a lumber drying facility via a hybrid Pythagorean fuzzy decision-making approach

Singer

2024

BioRes

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The strategic selection of facility locations plays a critical role in optimizing operational efficiency, reducing costs, and enhancing customer satisfaction, thereby contributing significantly to the success and competitiveness of businesses. In this study, an interval-valued Pythagorean fuzzy decision-making framework is proposed to select the best location for the lumber drying industry. A four-level hierarchical model is devised with four main criteria, 16 subcriteria, and five alternatives. The opinions of different experts are gathered to obtain input data. The weights of the criteria are calculated using the interval-valued Pythagorean fuzzy analytic hierarchy process (AHP) method. The interval-valued Pythagorean fuzzy weighted aggregated sum product assessment (WASPAS) method is employed to evaluate the alternative locations. A sensitivity analysis is conducted to support the validity of the model results. The study concludes by revealing the optimal location for the lumber drying industry in Turkey. This study presents its novelty by formulating the lumber drying facility location selection problem as a complex fuzzy multicriteria decision-making problem and integrating the Pythagorean fuzzy AHP and WASPAS methods to solve the problem.

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Section: Introductionmentioning

confidence: 99%