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About

Hu-Chen Liu is a prominent researcher specializing in multi-criteria decision-making (MCDM) methodologies, with a particular focus on their application to industrial robot evaluation and selection. His work addresses one of modern manufacturing's most pressing challenges: helping firms systematically identify optimal robotic solutions amid rapidly expanding product options and increasingly complex production demands. Liu's most significant contributions lie in developing and refining sophisticated linguistic and fuzzy decision frameworks. His pioneering 2013 paper on interval 2-tuple linguistic MCDM earned over 100 citations, establishing a foundational methodology for structured robot selection. He has since extended this work through innovative approaches integrating Pythagorean uncertain linguistic environments, hesitant fuzzy linguistic MULTIMOORA methods, and entropy-based combination weighting with Cloud TODIM techniques, collectively accumulating nearly 300 citations across his five most-cited works alone. What distinguishes Liu's research is his consistent effort to handle real-world complexities such as incomplete weight information and inherent decision uncertainty, making his frameworks practically applicable for manufacturing engineers and decision-makers. His body of work has meaningfully shaped how researchers and industry professionals approach robot selection problems, cementing his reputation as a leading authority at the intersection of fuzzy theory, linguistic computing, and intelligent manufacturing decision support.