Deadline Date: 31 December 2026
Engineering decision-making problems are inherently complex, often involving multiple conflicting criteria, uncertain or imprecise data, and dynamic environments. Numerical multi-criteria decision-making (MCDM) methods have emerged as essential tools to systematically evaluate and prioritize alternatives by quantifying and aggregating diverse criteria. These methods rely on rigorous mathematical frameworks and computational algorithms to support optimal or near-optimal decisions in engineering design, operations, and management.
However, real-world engineering problems frequently involve uncertainty, vagueness, and incomplete information, which classical numerical methods may not adequately capture. Fuzzy set theory and its extensions—such as intuitionistic fuzzy sets, neutrosophic sets, and rough sets—offer powerful means to model and process such uncertainty by allowing partial membership and flexible representations of imprecise data.
Simultaneously, advances in artificial intelligence (AI), including machine learning, evolutionary computation, swarm intelligence, and deep learning, have revolutionized decision-making by enabling adaptive, data-driven, and intelligent optimization strategies.
This Special Issue aims to provide a comprehensive platform for researchers, engineers, and practitioners to present state-of-the-art developments in numerical multi-criteria decision-making methods, fuzzy set theory and artificial intelligence for engineering applications. We invite original research articles, comprehensive reviews, and practical case studies that explore theoretical advancements, novel algorithmic designs, and innovative applications across various engineering domains.
Engineering decision-making problems are inherently complex, often involving multiple conflicting criteria, uncertain or imprecise data, and dynamic environments. Numerical multi-criteria decision-making (MCDM) methods have emerged as essential tools to systematically evaluate and prioritize alternatives by quantifying and aggregating diverse criteria. These methods rely on rigorous ... show more
Editorial board
