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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.
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    Development of dynamic balanced scorecard using case-based reasoning method and adaptive neuro-fuzzy inference system
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Khanmohammadi, Ehsan; Safari, Hossein; Zandieh, Mostafa; Malmir, Behnam; Tirkolaee, Erfan Babaee
    In recent years, selecting the strategies has been recognized as one of the most challenging issues facing senior and strategy managers of companies. In this regard, this article introduced an integrated framework that helps strategy managers to determine the organization's strategy by analyzing the long-term objectives and visions. The proposed methodology is based on developing and enhancing the performance of the balanced scorecard (BSC) by covering its limitations through being combined with system dynamics (SD) simulation, case-based reasoning (CBR) method, and adaptive neuro-fuzzy inference system (ANFIS) model. The SD model is built based on the company's strategy map to predict the future status of the company based on its selected strategies. The CBR method and ANFIS model are also to develop the sensitivity analysis and policy-making stage utilizing learning and human memory application. An Iranian food and beverage company is considered as a real-world case study to demonstrate how the proposed method could work and validate the research methodology's applicability. As the main finding, the model yields appropriate strategies with minor errors to reach the targets defined by managers. According to the proposed dynamic BSC, managers can select their strategies and observe their financial variables' outcomes on the planning horizon. Moreover, they can set future targets for their financial variable to receive strategies for their achievement.