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A method for stochastic multiple criteria decision making based on pairwise comparisons of alternatives with random evaluations. (English) Zbl 1206.90057

Summary: This paper proposes a method for solving stochastic multiple criteria decision making (MCDM) problems, where evaluations of alternatives on considered criteria are random variables with known probability density functions or probability mass functions. Probabilities on all possible results of pairwise comparisons of alternatives are first calculated using Probability Theory. Then, all possible results of pairwise comparisons are classified into superior, indifferent and inferior ones using a predefined identification rule. Consequently, the probabilities on all possible results of pairwise comparisons are partitioned into superior, indifferent and inferior probabilities. Furthermore, based on the derived probabilities, an algorithm is developed to rank the alternatives. Finally, a numerical example is used to illustrate the feasibility and validity of the proposed method.

MSC:

90B50 Management decision making, including multiple objectives
90C29 Multi-objective and goal programming
90C15 Stochastic programming
91B06 Decision theory

Software:

ELECTRE
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References:

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