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Supplier selection using the q-rung orthopair fuzzy based MAIRCA method

Betül Turanoğlu Şirin

Journal of Management Analytics2025https://doi.org/10.1080/23270012.2025.2515105article
AJG 2ABDC B
Weight
0.41

Abstract

The selection of suppliers is one of the most important strategic decisions that companies have to make in the context of supply chain management. While traditional methods cannot fully reflect the complexity of this process, fuzzy logic and multi-criteria decision-making (MCDM) deal more effectively with uncertainties and subjective evaluations. In this study, the q-rung orthopair fuzzy (q-ROF) based multi-attribute ideal-real comparative analysis (MAIRCA) method is proposed for supplier selection in an animal products company. To the best of our knowledge, this study represents the first application of the q-ROF MAIRCA method in the context of supplier selection. Also, this study demonstrates the ability of the q-ROF MAIRCA method to effectively manage uncertainty and subjectivity in decision-making processes. By incorporating the diverse opinions and preferences of decision-makers and systematically evaluating six suppliers based on criteria such as quality, product variety, price, and delivery time, the proposed method ensures robust and consistent results. The findings highlight the suitability of the q-ROF MAIRCA method for addressing complex supplier selection problems and its potential applicability in other industries with varying criteria.

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https://doi.org/https://doi.org/10.1080/23270012.2025.2515105

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@article{betül2025,
  title        = {{Supplier selection using the q-rung orthopair fuzzy based MAIRCA method}},
  author       = {Betül Turanoğlu Şirin},
  journal      = {Journal of Management Analytics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/23270012.2025.2515105},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.