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Erschienen in:
01.03.2014
SMAA-AD Model in Multicriteria Decision-Making Problems with Stochastic Values and Uncertain Weights
Erschienen in: Annals of Data Science | Ausgabe 1/2014
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Abstract
The current paper considers the stochastic multicriteria decision-making (MCDM) problems with multiple alternatives, stochastic criterion values and uncertain criterion weights. We propose SMAA-AD model and illustrate how SMAA-AD model is used in such stochastic MCDM problems. In SMAA-AD model, absolute dominant method is used to turn stochastic criterion values into deterministic absolute dominant values, and stochastic multicriteria acceptability analysis (SMAA) is used to rank the alternatives without foreknowing the decision maker’ preference on criterion weights. SMAA-AD model provides three indices, i.e., rank acceptability index, holistic acceptability index and central weight vector, to support the decision in the stochastic MCDM problems. SMAA-AD model overcomes some shortcomings of traditional MCDM methods. For example, it needs not to predefine any parameters and functions. We use a case of technology competition for cleaning polluted soil in Helsinki to illustrate our method.