A Comprehensive Literature Review of Smart Decision Support Systems and its Applications

Abstract

This paper is presented as a literature review for a comprehensive analysis of smart decision support systems (SDSS) and their different applications within different sectors. The paper traces the improvement of decision support systems (DSS) from their inception in the 1960s until their incorporation with artificial intelligence (AI) and machine learning (ML), resulting in more intelligent and adaptive SDSS. The paper shows the main architectures and base components of DSS and SDSS and highlights the value of data management, the value of model management, and user interface improvements. Special focus is given to multi-criteria decision making (MCDM) methods and their hybridizations with fuzzy, grey system and neutrosophic theories to mention uncertainty and complexity in decision environments. This paper classifies and summarizes recent researches using application domains like material and medical device selection, operations management, logistics, quality evaluation, supply chain management, risk management, and waste management. After this study, the findings indicate that combining MCDM techniques with AI improves decision-making process quality and organizational performance, particularly in complex and uncertain contexts. The paper also identifies current research gaps, like scalability, interoperability, and user interface challenges, and future directions, including deeper cooperation with big data technologies and IoT to further enhance the effectiveness and usability of SDSS.