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    A novel hybrid decision-making framework based on modified fuzzy analytic network process and fuzzy best–worst method
    (Springer Science and Business Media Deutschland GmbH, 2024) Khanmohammadi, Ehsan; Azizi, Maryam; Talaie, HamidReza; Ecer, Fatih; Tirkolaee, Erfan Babaee
    The development of decision-making frameworks is essential to improve the accuracy and efficiency of selecting the best options in complex scenarios. This research develops a novel efficient decision-making framework based on Fuzzy Analytic Network Process and Fuzzy Best–Worst Method. The motivation behind this work stems from the recognized challenges associated with establishing consistent Pairwise Comparison Matrices, a critical concern in the application of paired comparison analysis approaches. The primary objective is to overcome Pairwise Comparison Matrices inconsistencies, which can compromise the reliability of decision-making processes. To address this challenge, the study introduces a modified approach where variables are selectively compared with the best and worst counterparts, deviating from conventional methods that involve comprehensive comparisons among all variables. Innovatively, the research develops a nonlinear mathematical model-based methodology, to extract variable weights from Fuzzy Pairwise Comparison Matrices. The motivation behind this model is to elicit crisp weights with a reduced number of judgments from decision-makers, streamlining the decision-making process and mitigating the burden on stakeholders. The applicability and validity of the proposed approach are demonstrated through practical examples, including the resolution of a production line selection problem and sustainable supplier selection. By addressing real-world challenges, the study establishes the practical relevance and effectiveness of the developed decision-making method. Ultimately, the findings indicate that the research methodology is not only robust but also flexible, showcasing its adaptability to different decision-making scenarios. The findings reveal that in our suggested model, the reduction in pairwise comparisons is approximately 50% when compared to traditional methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.