Information Technology Reference
In-Depth Information
14
GMAA: A DSS Based on the Decision Analysis
Methodology - Application Survey and
Further Developments
Antonio Jiménez and Alfonso Mateos
Universidad Politécnica de Madrid
Spain
1. Introduction
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; Pérez 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
Search WWH ::
Custom Search