Li-Ming-Fan/pyDecision

A python MCDA Library - AHP; Fuzzy AHP; ARAS; Borda; BWM; CODAS; COPRAS; CRITIC; DEMATEL; Fuzzy DEMATEL; EDAS; Fuzzy EDAS; ELECTRE (I, I_s, I_v, II, III, IV, Tri-B); GRA; IDOCRIW; MABAC; MOORA; MOOSRA; MULTIMOORA; PROMETHEE (I, II, III, IV, V, VI, Gaia); SAW; SMART; TOPSIS; Fuzzy TOPSIS; VIKOR; Fuzzy VIKOR; WINGS; WSM; WPM; WASPAS

pyDecision

Introduction

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).

Usage

1. Install

pip install pyDecision

2. 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')

3. 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 )

4. Advanced MCDA Methods:

• 3MOAHP - Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods
• 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