Nuclear Waste Management Decision-Making Support with MCDA

Table 5

The most commonly used MCDM methods.

MCDA methods	MODM methods
Specific features
MCDA provides a comparison of a relatively small number of alternatives, prespecified by indicators/attributes, by ranking of alternatives. Decision-makers and experts’ preferences and intentions are embedded as weight and preference functions (single-attribute, or generalized criterion preference)	Within MODM, the numerous and not explicitly defined alternatives are simultaneously considered. Different optimization techniques with constraints are applied for searching for the best alternative. Multiple objective functions are to be defined which in general lead to vector-maximum problems
Classification of specific methods
Elementary methods; (i) Simple additive weighting; (ii) Kepner-Tregoe method	No preference methods; (i) Global criteria; (ii) Goal programming
Value-based methods; (i) MAVT; (ii) MAUT; (iii) AHP	A priori methods; (i) Criteria constraints method; (ii) The achievement scalarizing function; (iii) The weighted sum
Outranking methods; (i) ELECTRE; (ii) PROMETHEE; (iii) QUALIFLEX	A posteriori methods; (i) ADBASE; (ii) Normal constraint method; (iii) Directed search domain
Reference point based methods; (i) TOPSIS; (ii) VIKOR; (iii) BIPOLAR	Adaptive and interactive methods; (i) Genetic algorithms (NSGA-II, MOCHC, etc.); (ii) Feasible and reasonable goals methods; (iii) Parameter space investigation (PSI) method