SSM is based on a linear additive model assuming that the overall score of a given alternative is evacuated as the total sum of the performance score of each criterion multiplied by the weight of that criterion.
MAUT uses utility, that is, probabilities and their expected values, in assessing a single attribute preference function. Since the overall utility function for each alternative is a random variable, the ranking of alternatives within MAUT is based on the comparison of expected utilities: A certain alternative is more attractive than the other one if the expected value of the utility function for this alternative is greater than the expected values of the other ones. Lotteries must be applied.
Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) [14]
TOPSIS compares a set of alternatives by identifying weights for each criterion, normalizing scores for each criterion, and calculating the geometric distance between each alternative and the ideal and anti-ideal alternatives. TOPSIS is based on a concept that the more preferable alternative should have the shortest distance from the most desirable (ideal) alternative and the longest distance from the less desirable (anti-ideal) alternative.
Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) [15]
PROMETHEE is an outranking method which implies forming a partially ordered relation between each pair of alternatives. PROMETHEE is based on the generalization of criterion concept, with preference function assigned to each criterion; see Section 4.2.
AHP is based on three principles: decomposition, pairwise comparative judgments, and synthesis of priorities. Decomposition assumes the hierarchy elaboration for the given decision-making problem. Pairwise comparative judgments assume pairwise comparisons of alternatives against each criterion using AHP scale. Synthesis of priorities assumes determination of weights based on a pairwise comparison of criteria through hierarchy and determination of scores (eigenvectors for the maximum eigenvalue, evaluation of the overall score using a linear additive model).
