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About

Esin Mukul is a researcher at the forefront of applying multi-criteria decision-making (MCDM) methods to evaluate emerging technologies. Her primary research areas include smart health technologies, artificial intelligence in supply chain management, and hesitant fuzzy MCDM approaches. Mukul’s most significant contribution is her pioneering work on assessing smart health technologies, where she developed hesitant fuzzy linguistic MCDM frameworks to evaluate how smart technologies—such as big data-driven personalized treatments—can proactively solve healthcare challenges. Her 2020 paper on this topic has garnered 20 citations, reflecting its impact on the field of health technology assessment. She has also extended her expertise to supply chain management, co-authoring a 2022 study that uses fuzzy SAW-MOORA methods to identify success factors for AI applications in creating agile and resilient supply chains. Mukul’s work bridges theoretical MCDM advancements with practical evaluations of transformative technologies, offering decision-makers robust tools for prioritizing innovations in health and logistics. Her research continues to influence how hesitant fuzzy methods are applied to complex, real-world technology adoption problems.