A python library with the following MCDA methods: AHP (Analytic Hierarchy Process); Fuzzy AHP; ARAS (Additive Ratio ASsessment); Borda; BWM (Best-Worst Method); CODAS (Combinative Distance-based Assessment); COPRAS (Complex PRoportional Assessment); CRITIC (CRiteria Importance Through Intercriteria Correlation); DEMATEL (DEcision MAking Trial and Evaluation Laboratory); Fuzzy DEMATEL; EDAS (Evaluation based on Distance from Average Solution); Fuzzy EDAS; ELECTRE (I, I_s, I_v, II, III, IV, Tri-B); GRA (Grey Relational Analysis); IDOCRIW (Integrated Determination of Objective CRIteria Weights); MABAC (Multi-Attributive Border Approximation area Comparison); MOORA (Multi-Objective Optimization on the basis of Ratio Analysis); MOOSRA (Multi-Objective Optimisation on the Basis of Simple Ratio Analysis); MULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analisys Multiplicative Form); PROMETHEE (I, II, III, IV, V, VI, Gaia); SAW (Simple Additive Weighting); SMART (Simple Multi-Attribute Rating Technique); TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution); Fuzzy TOPSIS; VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje); Fuzzy VIKOR; WINGS (Weighted Influence Non-linear Gauge System); WSM (Weighted Sum Model); WPM (Weighted Product Model); WASPAS (Weighted Aggregates Sum Product Assessment).
- Install
pip install pyDecision- Import
# Import AHP
from pyDecision.algorithm import ahp_method
# Parameters
weight_derivation = 'geometric' # 'mean' or 'geometric'
# Dataset
dataset = np.array([
#g1 g2 g3 g4 g5 g6 g7
[1 , 1/3, 1/5, 1 , 1/4, 1/2, 3 ], #g1
[3 , 1 , 1/2, 2 , 1/3, 3 , 3 ], #g2
[5 , 2 , 1 , 4 , 5 , 6 , 5 ], #g3
[1 , 1/2, 1/4, 1 , 1/4, 1 , 2 ], #g4
[4 , 3 , 1/5, 4 , 1 , 3 , 2 ], #g5
[2 , 1/3, 1/6, 1 , 1/3, 1 , 1/3], #g6
[1/3, 1/3, 1/5, 1/2, 1/2, 3 , 1 ] #g7
])
# Call AHP Function
weights, rc = ahp_method(dataset, wd = weight_derivation)
# Weigths
for i in range(0, weights.shape[0]):
print('w(g'+str(i+1)+'): ', round(weights[i], 3))
# Consistency Ratio
print('RC: ' + str(round(rc, 2)))
if (rc > 0.10):
print('The solution is inconsistent, the pairwise comparisons must be reviewed')
else:
print('The solution is consistent')- Try it in Colab:
- AHP ( Colab Demo ) ( Paper )
- Fuzzy AHP ( Colab Demo ) ( Paper )
- ARAS ( Colab Demo ) ( Paper )
- Borda ( Colab Demo ) ( Paper )
- BWM ( Colab Demo ) ( Paper )
- CODAS ( Colab Demo ) ( Paper )
- COPRAS ( Colab Demo ) ( Paper )
- CRITIC ( Colab Demo ) ( Paper )
- DEMATEL ( Colab Demo ) ( Paper )
- Fuzzy DEMATEL ( Colab Demo ) ( Paper )
- EDAS ( Colab Demo ) ( Paper )
- Fuzzy EDAS ( Colab Demo ) ( Paper )
- ELECTRE I ( Colab Demo ) ( Paper )
- ELECTRE I_s ( Colab Demo ) ( Paper )
- ELECTRE I_v ( Colab Demo ) ( Paper )
- ELECTRE II ( Colab Demo ) ( Paper )
- ELECTRE III ( Colab Demo ) ( Paper )
- ELECTRE IV ( Colab Demo ) ( Paper )
- ELECTRE Tri-B ( Colab Demo ) ( Paper )
- GRA ( Colab Demo ) ( Paper )
- IDOCRIW ( Colab Demo ) ( Paper )
- MABAC ( Colab Demo ) ( Paper )
- MOORA ( Colab Demo ) ( Paper )
- MOOSRA ( Colab Demo ) ( Paper )
- MULTIMOORA ( Colab Demo ) ( Paper )
- PROMETHEE I ( Colab Demo ) ( Paper )
- PROMETHEE II ( Colab Demo ) ( Paper )
- PROMETHEE III ( Colab Demo ) ( Paper )
- PROMETHEE IV ( Colab Demo ) ( Paper )
- PROMETHEE V ( Colab Demo ) ( Paper )
- PROMETHEE VI ( Colab Demo ) ( Paper )
- PROMETHEE Gaia ( Colab Demo ) ( Paper )
- SAW ( Colab Demo ) ( Paper )
- SMART ( Colab Demo ) ( Paper )
- TOPSIS ( Colab Demo ) ( Paper )
- Fuzzy TOPSIS ( Colab Demo ) ( Paper )
- VIKOR ( Colab Demo ) ( Paper )
- Fuzzy VIKOR ( Colab Demo ) ( Paper )
- WINGS ( Colab Demo ) ( Paper )
- WSM, WPM, WASPAS ( Colab Demo ) ( Paper )
- Advanced MCDA Methods:
- 3MOAHP - Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods
- pyMissingAHP - A Method to Infer AHP Missing Pairwise Comparisons
- ELECTRE-Tree - Algorithm to infer the ELECTRE Tri-B method parameters
- Ranking-Trees - Algorithm to infer the ELECTRE II, III, IV and PROMETHEE I, II, III, IV method parameters