Intelligent Modeling and Decision Analytics (IMDA) is an international peer-reviewed journal dedicated to the development and application of intelligent models, analytical methods, and computational approaches for complex decision problems. The journal provides a multidisciplinary platform for research that integrates advanced modeling techniques with data, knowledge, uncertainty, and computational intelligence to support analysis, prediction, optimization, and decision-making.

IMDA is positioned at the intersection of intelligent modeling, decision analytics, artificial intelligence, computational intelligence, operations research, and optimization. The journal particularly encourages research that goes beyond the development of isolated algorithms or mathematical procedures and demonstrates how advanced models and analytical methods contribute to understanding complex systems and improving decision processes.

The journal views intelligent modeling broadly as the development of computational, mathematical, data-driven, knowledge-driven, and hybrid models capable of representing, analyzing, learning from, and reasoning about complex systems. This includes models based on artificial intelligence, machine learning, computational intelligence, fuzzy and uncertainty theories, optimization, simulation, and other advanced computational paradigms.

Decision analytics encompasses methods and frameworks that transform data, information, knowledge, and model outputs into meaningful analytical insight and decision support. IMDA therefore welcomes research in multi-criteria and multi-objective decision-making, prescriptive analytics, optimization, risk and uncertainty analysis, forecasting, intelligent decision-support systems, and related analytical methodologies.

A central objective of IMDA is to strengthen the connection between methodological and computational innovation and meaningful decision applications. New models, algorithms, optimization procedures, artificial intelligence methods, uncertainty representations, weighting or ranking techniques, and analytical frameworks should clearly identify the problem they address and demonstrate their methodological, computational, or practical contribution.

The journal welcomes both theoretical and application-oriented research, particularly studies that integrate multiple analytical paradigms. Contributions combining artificial intelligence and machine learning with decision analysis, optimization, operations research, fuzzy and uncertainty modeling, simulation, forecasting, or expert knowledge are especially encouraged.

IMDA supports applications across a broad range of complex systems and decision environments, including management and business, engineering and industrial systems, transportation and logistics, supply chains, energy and sustainability, environmental systems, healthcare, risk and resilience, smart systems, finance, public-sector decision-making, and other areas in which intelligent modeling and analytical decision support provide meaningful value.

Topics of Interest

Topics of interest include, but are not limited to:

• intelligent modeling and computational intelligence;
• decision analytics and decision-support systems;
• artificial intelligence and machine learning for decision-making;
• data-driven, knowledge-driven, and hybrid modeling;
• multi-criteria and multi-objective decision-making;
• optimization, metaheuristics, and intelligent optimization;
• operations research and computational decision models;
• fuzzy, rough, grey, neutrosophic, and other uncertainty-based models;
• probabilistic, possibilistic, and uncertainty-aware analytics;
• predictive and prescriptive analytics;
• forecasting, classification, clustering, and pattern recognition;
• explainable and interpretable artificial intelligence;
• expert systems, knowledge-based systems, and recommendation systems;
• simulation, digital twins, and complex systems modeling;
• risk, reliability, resilience, and uncertainty analysis;
• information fusion, aggregation, weighting, ranking, and preference modeling;
• human-centered and expert-based decision analytics;
• large language models and generative AI for modeling, analytics, and decision support;
• intelligent analytics for management, engineering, transportation, logistics, supply chains, energy, sustainability, healthcare, finance, environmental, and industrial systems.

Types of Contributions

IMDA welcomes original research articles, methodological studies, computational investigations, application-driven studies, and high-quality review articles that contribute to the development or application of intelligent modeling and decision analytics.

Editorial Perspective

The defining question for manuscripts submitted to Intelligent Modeling and Decision Analytics is whether the proposed model, method, or analytical framework provides a meaningful contribution to intelligent modeling, decision analytics, or their integration in solving complex decision problems.