Method MCDM

Method MCDM

1. ELECTRE

   • Advantage: Takes uncertainty and vagueness into account.
   • Disadvantage: Its process and outcome can be difficult to explain in layman’s terms; outranking causes the strengths and weaknesses of the alternatives to not be directly identified.

2. PROMETHEE

   • Disadvantage: Does not provide a clear method by which to assign weights.
   • Advantage: Easy to use; does not require assumption that criteria are proportionate.

3. TOPSIS

   • Advantage: Has a simple process; easy to use and program.
   • Disadvantage: Its use of Euclidean Distance does not consider the correlation of attributes

4. CBR

   • Advantage: Not data intensive; requires little maintenance; can improve over time; can adapt to changes in environment.
   • Disadvantage: Sensitive to inconsistent data; requires many cases.

5. MAUT

   • Advantage: Takes uncertainty in to account ; can incorporate preferences.
   • Disadvantage: Needs a lot of input ; preferences need to be precise.

6. GP

   • Adventge: Capable of handling large-scale problems; can produce infinite alternatives.
   • Disadvantage: It's ability to weight coefficients typically needs to be u in combination with other MCDM methods to weight coefficients.

7. AHP

   • Advantage: Easy to use; scalable; hierarchy structure can easily adjust to fit many sized problems; not data intensive.
   • Disadvantage: Problems due to interdependence between criteria and alternatives; can lead to inconsistencies between judgment and ranking criteria; rank reversal