Fuzzy Analytics Spectrum (FAS) is an international peer-reviewed journal dedicated to analytics under uncertainty, with a particular focus on how fuzzy modelling can enhance the interpretation, analysis, prediction, evaluation, and optimization of complex systems.

FAS is positioned at the intersection of fuzzy modelling, data analytics, decision analytics, artificial intelligence, and optimization. Rather than serving as a general outlet for fuzzy-set theory, the journal focuses on the analytical value of fuzzy approaches: how uncertain, imprecise, incomplete, or linguistically expressed information can be transformed into meaningful insights and practically useful decisions.

The journal promotes the concept of fuzzy analytics as an integrated analytical perspective in which uncertainty modelling forms part of a broader process involving data and information representation, pattern and relationship analysis, prediction, evaluation, optimization, and decision support. Contributions should therefore demonstrate not only how uncertainty is represented, but also what additional analytical insight is obtained through fuzzy modelling and how that insight improves understanding or decision-making.

A central objective of FAS is to bridge the gap between methodological advances in uncertainty modelling and their effective use in contemporary analytical environments. The journal particularly welcomes research combining fuzzy approaches with artificial intelligence, machine learning, data-driven modelling, multi-criteria decision analytics, optimization, operations research, simulation, forecasting, and intelligent decision-support systems.

FAS encourages methodological and computational innovation when it is motivated by an identifiable analytical problem. The introduction of a new fuzzy structure, aggregation mechanism, distance or similarity measure, weighting procedure, or ranking technique should be accompanied by a clear explanation of the analytical need it addresses and evidence of the added value it provides. Mathematical novelty alone, without a meaningful analytical contribution, is not the primary focus of the journal.

The journal places particular emphasis on real-world analytics in areas where uncertainty is an inherent component of the problem. These include management and business systems, transportation and logistics, industrial and engineering systems, environmental and waste management, energy and sustainability, supply chains, risk and resilience, smart systems, and other complex socio-technical environments.

Topics of Interest

Topics of interest include, but are not limited to:

• fuzzy analytics and analytics under uncertainty;
• fuzzy-enhanced data and decision analytics;
• integration of fuzzy modelling with artificial intelligence and machine learning;
• uncertainty-aware predictive and prescriptive analytics;
• fuzzy optimization and computational analytics;
• fuzzy multi-criteria and multi-objective decision analytics;
• intelligent decision-support and recommendation systems;
• fuzzy forecasting, classification, clustering, and pattern analysis;
• uncertainty-aware risk, resilience, and reliability analytics;
• fuzzy information fusion and knowledge-based analytics;
• explainable and interpretable analytics under uncertainty;
• human-centred analytics incorporating linguistic and expert information;
• hybrid data-driven and knowledge-driven analytical models;
• analytics for management, transportation, logistics, engineering, sustainability, energy, environmental and industrial systems.

Types of Contributions

FAS welcomes original research articles, methodological studies, computational investigations, application-driven studies, and high-quality review articles that advance the understanding or practice of analytics under uncertainty.

Editorial Perspective

The defining question for manuscripts submitted to Fuzzy Analytics Spectrum is not simply whether they use fuzzy methods, but whether: