Intelligent Decision Support Systems—An Analysis of Machine Learning and Multicriteria Decision-Making Methods

Abstract

Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions.

Conversing with business process-aware Large Language Models: the BPLLM framework

“…The MDP is also used in [14] to realize a DSS for declarative artifact-centric process models. Other recent studies focused on the identification of new DSSs [2] that radically evolve to capture the new opportunities derived by the emerging Artificial Intelligence models [15,16]. In this direction, authors in [2] introduce an end-to-end process-aware DSS able to predict the decisions and explain which factors influence the prediction.…”

“…In the DFG case, fixed-size chunking and recursive chunking with different sizes (16,64,128,256,512) are tested. For the BPMN format also a tailored chunking is evaluated.…”

Comparative Assessment of Analytical Hierarchy Process (AHP) and Fuzzy Overlay Analysis (FOA) Models in Groundwater Potential Zone Mapping Using Sensitive Analysis: A GIS-RS Integrated Approach

“…Kappa value (k) is de ned by the following formula, which quanti es the agreement between raters beyond what would be expected by chance alone. This statistical measure is particularly valuable in research settings where multiple observers or coders are involved, as it provides insight into the reliability of their judgments (Ali et al, 2023). By assessing inter-rater dependability, researchers can gauge the consistency of their data collection process, thereby enhancing the overall validity and robustness of their ndings.…”

Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision

“…Decision support systems are also important [47]. Based on the predicted indicators, in our case it is possible to predict the optimal redirection of flows, safe or short paths, etc.…”