Please enable JavaScript.
Coggle requires JavaScript to display documents.
How to support the application of multiple criteria decision analysis? -…
How to support the application of multiple criteria decision analysis?
A taxonomy of the MCDA process characteristics
Phase 1
Problem formulation
Problem type
Problem statement
Choice
Ranking
Sorting
The set of alternatives
Stable
Incremental
Criteria
Structure
Flat
Hierachical
Evaluation of performance
Measurement scale
Ordinal
Interval
Ratio
Perfomance type
Deterministic
Uncertain
Phase 2
Construction of decision recommendation
Features of aggregation
Compensation level between criteria
MCDA method decision context dependency
MCDA method capacity to handle inconsistent preferences
Exploitation of the preference model
Univocal recommendation
Robustness analysis
Exact
Stochastic
Group decision
Elicitation of preferences
Direct
Pairwise comparison thresholds
Criteria interactions
No weights
Preference models
Weight types
Indirect
Frequency of preference provision
Elicitation approach
Phase 3
Qualitative features and technical support
Easiness of method's use
Extent of use of the method in the specific context/area
number of alternatives and/or criteria the method can work with
Software support and graphical representation
Processing time needed to compile the data required for the method
Clustering of DSSs for MCDA method(s) recommendation
Most commonly used
Rules-based systems(78,3%)
Algorithm (17,4%)
Artificial neural network (4,3%)