Operations research is at the core of decision making, for a long time. However, in the present day changing and dynamic interactions in organizations result in a high degree of uncertainty. To have a reasonable solution to the problem of dealing with uncertain situations, organizations need a suitable decision-making process at each stage. Traditional decision support systems (DSS) could only enable decision making through data modelling and numerical calculations and lack in integrating qualitative, quantitative and predictive analysis to provide near-to-human intelligence in decision making. Furthermore, there are real-world applications which need consideration and analysis based on multiple criteria and features, which in turn make the decision process more complex.

Artificial intelligence (AI) has the capability to provide autonomy and flexibility in such a dynamic and multicriteria decision making environment. AI empowers organizations and researchers to make intelligent decisions because its integration to operations research would enhance capabilities of the DSS. To establish such intelligent DSS, AI-enabled diverse programming paradigms and frameworks are required. The practitioners, decision-makers and researchers need to develop relevant scientific theories, methodologies, and algorithms, such as machine learning, deep learning, data mining, reasoning, inference, multi-criteria data analysis etc., for better decision making. Researchers concerned with the design and development of AI-DSS seek to demonstrate innovative scientific techniques, tools and models, rather than relying on conventional approaches, to improve the quality and accuracy of the intended decisions.

The aim of this Special Issue is to present high-quality original research and review articles that cover novel, cutting-edge technologies and methods concerned with the scientific design, development and implementation of AI-DSS using latest development in the area of AI and multi-criteria decision making (MCDM). We invite researchers working on how to integrate AI with MCDM, with the objective to improve the quality and accuracy of the decisions generated by these systems across a range of diverse applications. Submissions can include different MCDM methods such as preference ranking organization method for enrichment evaluation (PROMETHEE), analytic hierarchy process (AHP), analytic network process (ANP), technique for order of preference by similarity to ideal solution (TOPSIS), and elimination and choice translating reality (ELECTRE).

Potential topics include but are not limited to the following:

• Artificial intelligence learning techniques and algorithms (e.g., machine and deep learning) for the development of AI-DSS across a wide range of sectors (e.g., business, education, healthcare, etc.)
• Data mining and knowledge discovery techniques (e.g., social network analysis, web mining, crowdsourcing, association rules mining, prediction and classification, clustering, and regression)
• Data mining and knowledge discovery techniques for scientific design, development and implementation of AI-DSS
• Reasoning and recommendation techniques and algorithms (e.g., rule-based, case-based reasoning, adaptive reasoning, inference, knowledge-based recommendation, contents-based recommendation and collaborative recommender methods) for decision support systems
• Analytics techniques (e.g., predictive analytics, big data analytics, etc.) for supporting DSS through predating and visualizing the data
• Uncertainty handling techniques (e.g., fuzzy sets, rough sets and adaptive rough sets to handle dynamic situations of the DSS)
• Comparison, survey and implementation of different MCDM methods (PROMETHEE, AHP, ANP, TOPSIS, and ELECTRE) in scientifically analyzing data on the basis of multiple criteria to develop more accurate and intelligent decision support systems
• Collaborative decision making and grouped decision-making techniques for development of AI-DSS
• Multi-objective, criteria optimization and weighting in multi-criteria data analysis for decision making
• Weighting, sorting and ranking criteria for selecting best alternatives out of the available candidates to accurately design and develop the decision support system
• Scientific approach to the development of knowledge-driven, data-driven, model-driven, and hybrid decision support systems in different real-world applications
• Applications of AI and MCDM in medicine and healthcare, businesses, e-commerce and marketing, banking, stock market and finance
• Decision support systems in agriculture, weather forecasting, tourism and hospitality, education, psychology, entertainment and communications
• Scientific development of AI-DSS in the fields of command and control, cybersecurity and predictive maintenance
• Artificial Intelligence in social media analysis, military and defense, autonomous mobility, robo-advisory (robotics), engineering, industries and manufacturing