A simulation-based test of stochastic multicriteria acceptability analysis using achievement functions

摘要: Stochastic multicriteria acceptability analysis using achievement functions (SMAA-A) is a preference model for discrete-choice decision making that inverts the traditional goal programming process by asking what combinations of aspirations are necessary to make each alternative preferred one, rather than given set aspirations. In this paper, we test ability discern good-performing alternatives from poorly-performing ones simulation study. Simulation results show suitably detailed construction index particularly important, and resulting can be fruitfully applied in selection shortlist larger with only very limited maker involvement.