Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008

Multi-attribute decision analysis (MADA) deals with the complexities of choosing from a pool of alternatives a decision based on multiple, usually conflicting criteria. MADA has become even more complex as the number of alternatives becomes larger. This study deals with this problem by introducing reduction of alternatives as a step to arrive at a decision in MADA. Clustering approach on reduction and the ideal point d-coverage were two reduction approaches developed in this study. These new approaches were evaluated along the classical dominance analysis and range of acceptability, which are both tools for reduction. These methods were evaluated depending on how much reduction is done without sacrificing potentially optimal alternatives. Empirical evidences derived from applying the methods to two real-world problems, two-crop successions in Kapatagan and coconut shell charcoal in PCA, show that alternatives can be reduced significantly while preserving potentially optimal alternatives.