Stochastic Multicriteria Acceptability Analysis Using Achievement Functions

We consider multiple criteria decision-making problems where a group of decision-makers wants to find the most preferred solution from a discrete set of alternatives. We develop a method that uses achievement scalarizing functions for charting subsets of reference points that would support a certain alternative to be the most preferred one. The resulting descriptive information is provided to the decision-makers in the form of reference acceptability indices and central reference points for each decision alternative. Then, the decision-makers can compare this information with their own preferences. We demonstrate the use of the method using a strategic multiple criteria decision model for an electricity retailer.