This chapter provides an overview of decision support systems (DSS), their history, characteristics, and various decision-making methods. DSS are systems designed to assist decision-makers in semi-structured and unstructured problems, complementing human judgment with computer technology. The chapter highlights the increasing complexity of decision-making due to technological advancements and the need for more sophisticated tools to handle large volumes of data and complex problems. The history of DSS is traced from the early 1960s, when the concept of DSS emerged, to the present, noting significant milestones such as the development of model-oriented DSS and the integration of knowledge-based systems. Key components of DSS, including language systems, presentation systems, knowledge systems, and problem-processing systems, are discussed. The chapter also outlines the ideal characteristics of DSS, emphasizing their ability to support decision-makers in semi-structured and unstructured problems, and their applicability across various managerial levels. It defines structured, semi-structured, and unstructured decisions and explains how DSS can improve the effectiveness and efficiency of decision-making processes. Additionally, the chapter introduces multi-attribute decision-making (MADM) methods, including elementary methods (such as Maximin, Maximax, Pros and Cons analysis, Conjunctive and Disjunctive methods, and Lexicographic method) and more advanced methods like Multi-Attribute Utility Theory (MAUT) and outranking methods (such as ELECTRE and PROMETHEE). Each method is described in detail, highlighting its strengths and limitations. Finally, the chapter discusses sensitivity analysis, which assesses the impact of changes in input parameters on the final decision, and provides a summary of the key points covered.This chapter provides an overview of decision support systems (DSS), their history, characteristics, and various decision-making methods. DSS are systems designed to assist decision-makers in semi-structured and unstructured problems, complementing human judgment with computer technology. The chapter highlights the increasing complexity of decision-making due to technological advancements and the need for more sophisticated tools to handle large volumes of data and complex problems. The history of DSS is traced from the early 1960s, when the concept of DSS emerged, to the present, noting significant milestones such as the development of model-oriented DSS and the integration of knowledge-based systems. Key components of DSS, including language systems, presentation systems, knowledge systems, and problem-processing systems, are discussed. The chapter also outlines the ideal characteristics of DSS, emphasizing their ability to support decision-makers in semi-structured and unstructured problems, and their applicability across various managerial levels. It defines structured, semi-structured, and unstructured decisions and explains how DSS can improve the effectiveness and efficiency of decision-making processes. Additionally, the chapter introduces multi-attribute decision-making (MADM) methods, including elementary methods (such as Maximin, Maximax, Pros and Cons analysis, Conjunctive and Disjunctive methods, and Lexicographic method) and more advanced methods like Multi-Attribute Utility Theory (MAUT) and outranking methods (such as ELECTRE and PROMETHEE). Each method is described in detail, highlighting its strengths and limitations. Finally, the chapter discusses sensitivity analysis, which assesses the impact of changes in input parameters on the final decision, and provides a summary of the key points covered.