In this work, a methodology for multicriteria analysis is provided, in which the alternative evaluation function is multiplicative. Starting from the decision matrix with numerical values, a normalization procedure is applied to transform the values of this matrix into a normalized matrix ranging between minus and plus one. The factors of the function used to evaluate alternatives are constructed from the values in the normalized matrix with the weighting coefficients of this method (the procedure is provided). Weight coefficients, which are positive numbers and do not sum to one, are obtained through additive weight coefficients with a sum of one (a methodology for this is provided here). The factors of the function used to evaluate alternatives are numbers greater than one if the values from the normalized matrix are positive. For negative values (of the same absolute value), the factors are reciprocal values of the factors for positive values.

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New Multiplicative Method of Multicriteria Analysis

  • Mališa R. Žižović,
  • Dragan Pamučar,
  • Miodrag M. Žižović,
  • Miloljub Albijanić

摘要

In this work, a methodology for multicriteria analysis is provided, in which the alternative evaluation function is multiplicative. Starting from the decision matrix with numerical values, a normalization procedure is applied to transform the values of this matrix into a normalized matrix ranging between minus and plus one. The factors of the function used to evaluate alternatives are constructed from the values in the normalized matrix with the weighting coefficients of this method (the procedure is provided). Weight coefficients, which are positive numbers and do not sum to one, are obtained through additive weight coefficients with a sum of one (a methodology for this is provided here). The factors of the function used to evaluate alternatives are numbers greater than one if the values from the normalized matrix are positive. For negative values (of the same absolute value), the factors are reciprocal values of the factors for positive values.