GMAA: A DSS Based on the Decision Analysis Methodology - Application Survey and Further Developments

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Most of the real decision-making problems that we face nowadays are complex in the sense that they are usually plagued with uncertainty, and we have to take into account several conflicting objectives simultaneously. Decision support systems (DSS) play a key role in these situations helping decision makers (DMs) to structure and achieve a better understanding of the problem to make a final decision. A number of multicriteria decision analysis techniques have underpinned the development and implementation of DSS in the last few decades (Figueira et al. (eds.), 2005). They include the analytic hierarchy process (Saaty, 1980; Perez et al., 2006; Bana e Costa & Vansnick, 2008); outranking methods, especially ELECTRE (Roy, 1996; Figueira et al., 2005; Wang & Triantaphyllou, 2008); PROMETHEE, proposed in (Brans & Vincke, 1985; Brans et al., 1996; Brans & Mareschal, 2005; Mareschal et al., 2008), and their variants; or approaches based on multi-attribute utility theory (MAUT) (Keeney & Raiffa, 1993; Clemen 1996). Corner and Kirkwood and Keefer et al. offer a systematic review of applications fields where DA methods were used and reported in operations research journals between 1970 and 2001 (Corner & Kirkwood, 1991; Keefer et al., 2004). OR/MS Today published its tenth biennial decision analysis software survey in 2010 (Bucksaw, 2010). These surveys include broad-based information about available DSSs and their features. The decision analysis (DA) methodology is widely used within MAUT. The goal of DA is to structure and simplify the task of making hard decisions as much as possible (Keeney & Raiffa, 1976; Clemen, 1996, Kirkwood, 1997). DA was developed on the assumption that the alternatives will appeal to the expert, depending on the expert preferences concerning the possible performances and the likelihood of each alternative performing in this manner. What makes DA unique is the way in which these factors are quantified and formally incorporated into the problem analysis. Existing information, collected data, models and professional judgments are used to quantify the likelihoods of a range of performances, whereas utility theory is used to quantify preferences. The usual or traditional approach to DA calls for single or precise values for the different model inputs, i.e., for the weight and utility assessments, as well as for the multi-attributed performances of the alternatives. However, most complex decision-making problems