# pyDecision
## Introduction
A python library with the following MCDA methods: **AHP** (Analytic Hierarchy Process); **Fuzzy AHP**; **PPF AHP** (Proportional Picture Fuzzy AHP); **ARAS** (Additive Ratio ASsessment); **Fuzzy ARAS**; **Borda**; **BWM** (Best-Worst Method); **Simplified BWM**; **Fuzzy BWM**; **CILOS** (Criterion Impact LOSs); **CoCoSo** (COmbined COmpromise SOlution); **CODAS** (Combinative Distance-based Assessment); **Copeland**; **COPRAS** (Complex PRoportional Assessment); **Fuzzy COPRAS**; **CRADIS** (Compromise Ranking of Alternatives from Distance to Ideal Solution); **CRITIC** (CRiteria Importance Through Intercriteria Correlation); **Fuzzy CRITIC**; **DEMATEL** (DEcision MAking Trial and Evaluation Laboratory); **Fuzzy DEMATEL**; **EDAS** (Evaluation based on Distance from Average Solution); **Fuzzy EDAS**; **Entropy**; **ELECTRE** (I, I_s, I_v, II, III, IV, Tri-B); **FUCOM** (Full Consistency Method); **Fuzzy FUCOM**; **GRA** (Grey Relational Analysis); **IDOCRIW** (Integrated Determination of Objective CRIteria Weights); **MABAC** (Multi-Attributive Border Approximation area Comparison); **MACBETH** (Measuring Attractiveness by a Categorical Based Evaluation TecHnique); **MAIRCA** (Multi-Attributive Ideal-Real Comparative Analysis); **MARA** (Magnitude of the Area for the Ranking of Alternatives) ; **MARCOS** (Measurement of Alternatives and Ranking according to COmpromise Solution); **MAUT** (Multi-attribute Utility Theory); **MEREC** (MEthod based on the Removal Effects of Criteria); **MOORA** (Multi-Objective Optimization on the basis of Ratio Analysis); **Fuzzy MOORA**; **MOOSRA** (Multi-Objective Optimisation on the Basis of Simple Ratio Analysis); **MULTIMOORA** (Multi-Objective Optimization on the basis of Ratio Analisys Multiplicative Form); **OCRA** (Operational Competitiveness RAting); **Fuzzy OCRA** ; **OPA** (Ordinal Priority Approach); **ORESTE** (Organisation Rangement Et SynThesE de donnees relationnelles); **PIV** (Proximity Indexed Value); **PROMETHEE** (I, II, III, IV, V, VI, Gaia); **EC PROMETHEE**; **RAFSI** (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval); **REGIME** (REGIonal Multicriteria Elimination); **ROC** (Rank Ordered Centroid); **ROV** (Range Of Value); **RRW** (Rank Reciprocal Weighting); **RSW** (Rank Summed Weight); **SAW** (Simple Additive Weighting); **SECA** (Simultaneous Evaluation of Criteria and Alternatives); **SMART** (Simple Multi-Attribute Rating Technique); **SPOTIS** (Stable Preference Ordering Towards Ideal Solution); **TODIM** (TOmada de Decisao Interativa e Multicriterio - Interactive and Multicriteria Decision Making); **PSI** (Preference Selection Index); **MPSI** (Modified Preference Selection Index); **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); **WISP** (Integrated Simple Weighted Sum Product); **Simple WISP**; **WSM** (Weighted Sum Model); **Fuzzy WSM**; **WPM** (Weighted Product Model); **Fuzzy WPM**; **WASPAS** (Weighted Aggregates Sum Product Assessment); **Fuzzy WASPAS**.
pyDecision offers an array of features, including the **comparison of ranking alternatives** and **comparison of criterion weights** from various methods. The library is also fully integrated with **chatGPT**, elevating result interpretation through AI. Additionally, pyDecision provides the flexibility to import results from custom methods or those not yet implemented in the library for swift comparison.
## Citation
PEREIRA, V.; BASILIO, M.P.; SANTOS, C.H.T (2024). Enhancing Decision Analysis with a Large Language Model: pyDecision a Comprehensive Library of MCDA Methods in Python. arXiv. https://arxiv.org/abs/2404.06370
## Usage
1. Install
```bash
pip install pyDecision
```
2. Import
```py3
# Import AHP
from pyDecision.algorithm import ahp_method
# Parameters
weight_derivation = 'geometric' # 'mean'; 'geometric' or 'max_eigen'
# 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 ](https://colab.research.google.com/drive/1qwFQs5xkTZ8K-Ul_wWcCtPjLH0QooU9g?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1016/0377-2217(90)90057-I))
- Fuzzy AHP ([ Colab Demo ](https://colab.research.google.com/drive/1RtEMOLGL5wtmheMRZv8emcO5wbjYVBCo?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/S0165-0114(83)80082-7))
- PPF AHP ([ Colab Demo ](https://colab.research.google.com/drive/1wI-8z2aysGKhSI3PLxN6vOZ4jsbPmKYl?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.eswa.2023.122354))
- ARAS ([ Colab Demo ](https://colab.research.google.com/drive/1rwQgXjvC3E6pRhOs7CkcCV8Vw2bXEPLy?usp=sharing)) ( [ Paper ](https://doi.org/10.3846/tede.2010.10))
- Fuzzy ARAS ([ Colab Demo ](https://colab.research.google.com/drive/1kZDkEWsw0d0nFhDQQk8azZXRod7RnfZr?usp=sharing)) ( [ Paper ](https://doi.org/10.3846/transport.2010.52))
- Borda ([ Colab Demo ](https://colab.research.google.com/drive/1t5RVtG7_yXK-nPxM0MVd4U01qfTQYW4k?usp=sharing)) ( [ Paper ](http://gerardgreco.free.fr/IMG/pdf/MA_c_moire-Borda-1781.pdf))
- BWM ([ Colab Demo ](https://colab.research.google.com/drive/1XkacTmtSBvZmx_5K9cfz8t1Ao5j-D-bZ?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.omega.2014.11.009))
- Simplified BWM ([ Colab Demo ](https://colab.research.google.com/drive/1v3QfSdprM8gwxL4VWmh75mPiPn2YWOZn?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/su13084487))
- Fuzzy BWM ([ Colab Demo ](https://colab.research.google.com/drive/1hBTXyOLpBoC7oE-hsolPH2O0ekzp4VU0?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.knosys.2017.01.010))
- CILOS ([ Colab Demo ](https://colab.research.google.com/drive/1RnSqO_VEPyvXAMHdneloYvA0TzPx55kw?usp=sharing)) ( [ Paper ](https://doi.org/10.1142/S0219622016500036))
- CoCoSo ([ Colab Demo ](https://colab.research.google.com/drive/1U8a3NZzQaxDkJdUT3uKIeeoqFtT_3Mnx?usp=sharing)) ( [ Paper ](https://doi.org/10.1108/MD-05-2017-0458))
- CODAS ([ Colab Demo ](https://colab.research.google.com/drive/1hm7__urqFeBHM6nVQJcBzGPF72DFuoLr?usp=sharing)) ( [ Paper ](https://ideas.repec.org/a/cys/ecocyb/v50y2016i3p25-44.html))
- Copeland ([ Colab Demo ](https://colab.research.google.com/drive/1ObP3AkQAzoCxT6et5Qkyk1trlER7mcdH?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/BF01212012))
- COPRAS ([ Colab Demo ](https://colab.research.google.com/drive/1TZJtSjXqwYEwuL7-wfLcPQ8ZBtDq3lth?usp=sharing)) ( [ Paper ](https://doi.org/10.3846/20294913.2012.762953))
- Fuzzy COPRAS ([ Colab Demo ](https://colab.research.google.com/drive/1AIGgxBkmcA6YHKx06VeYcGf2EV8dPffW?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/s00500-021-05762-w))
- CRADIS ([ Colab Demo ](https://colab.research.google.com/drive/1p7AQmPIOsZFxaypqMsiRIWW8mIvDtoLi?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/s10668-021-01902-2))
- CRITIC ([ Colab Demo ](https://colab.research.google.com/drive/1D5SaBHa1-Eo_KYSXHkFjsHYu29M21l_F?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0305-0548(94)00059-H))
- Fuzzy CRITIC ([ Colab Demo ](https://colab.research.google.com/drive/1wofFhWDw6fn-XpLQoQjveKjyaId-AsEw?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.engappai.2022.104942))
- DEMATEL ([ Colab Demo ](https://colab.research.google.com/drive/1T04qEft9uwTyQx--gADN6V_vUrT21Xo6?usp=sharing)) ( [ Paper ](https://doi.org/10.1155/2018/3696457))
- Fuzzy DEMATEL ([ Colab Demo ](https://colab.research.google.com/drive/15e9dMDROr3cxjbWRXg3_t4TScuQtQDpR?usp=sharing)) ( [ Paper ](https://www.sciencedirect.com/science/article/abs/pii/S0957417405003593))
- EDAS ([ Colab Demo ](https://colab.research.google.com/drive/1xsMdwH-IH-zvOW-1kv6ztQnKGt7p5JnY?usp=sharing)) ( [ Paper ](https://doi.org/10.15388/Informatica.2015.57))
- Fuzzy EDAS ([ Colab Demo ](https://colab.research.google.com/drive/1kw2LwztNAU9Asjj6BvBmvk11wvk8R3V6?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/978-981-32-9072-3_63))
- Entropy ([ Colab Demo ](https://colab.research.google.com/drive/1LOCef2KFxoV2qUEQRi4DqfzrgnMgtwT9?usp=sharing)) ( [ Paper ](https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf))
- ELECTRE I ([ Colab Demo ](https://colab.research.google.com/drive/1KFqRPBRyv-fxiu2B1y7VNkP5pCCbILF1?usp=sharing)) ( [ Paper ](https://github.com/Valdecy/Datasets/blob/master/MCDA/E01.pdf))
- ELECTRE I_s ([ Colab Demo ](https://colab.research.google.com/drive/1ngxsQPh2QULjd1_AifFofbukq5zIOePd?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))
- ELECTRE I_v ([ Colab Demo ](https://colab.research.google.com/drive/1moonq95gqXqmbRe2KvgqbN2IfowJ12C-?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))
- ELECTRE II ([ Colab Demo ](https://colab.research.google.com/drive/1UeAjICH6_tjVr3O9H-fC65HHYMVZgTKc?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))
- ELECTRE III ([ Colab Demo ](https://colab.research.google.com/drive/1smeD5ZoPgBnAAUyooAXSrkxHgqZPmUC9?usp=sharing)) ( [ Paper ](https://github.com/Valdecy/Datasets/raw/master/MCDA/E03.pdf))
- ELECTRE IV ([ Colab Demo ](https://colab.research.google.com/drive/178x062yC-Es6lstEiFaFprbMsTJZwnC-?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))
- ELECTRE Tri-B ([ Colab Demo ](https://colab.research.google.com/drive/1hu0fJcxdBAiEDrVngmKQfpINpjTF-osE?usp=sharing)) ( [ Paper ](https://drive.google.com/file/d/1oWOI_sX3EEYdRbavoBTT7vUmPII1yPgE/view?usp=sharing))
- FUCOM ([ Colab Demo ](https://colab.research.google.com/drive/1eWP3xf3-9iLLW_l_9JuAe6BEeoMsqzcL?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/sym10090393))
- Fuzzy FUCOM ([ Colab Demo ](https://colab.research.google.com/drive/1bkelWth_7TOW_gIz8mBNe_4W5Ox84FUB?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/su14094972 ))
- GRA ([ Colab Demo ](https://colab.research.google.com/drive/1aMMI0Cuo5kpzTDefqEwJhf0wWpBOP_JL?usp=sharing)) ( [ Paper ](https://uranos.ch/research/references/Julong_1989/10.1.1.678.3477.pdf))
- IDOCRIW ([ Colab Demo ](https://colab.research.google.com/drive/1zt8uPFZGcHaSnpiT7tDnrDjvs0pK_7vS?usp=sharing)) ( [ Paper ](https://doi.org/10.1142/S0219622016500036))
- MABAC ([ Colab Demo ](https://colab.research.google.com/drive/1BMqO-HnBXdcOZfZoULpx1H4MLPoUGucJ?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.eswa.2014.11.057))
- MACBETH ([ Colab Demo ](https://colab.research.google.com/drive/1GqM9uPgbaWCGyj4l-XjkoifY2JJoVyf2?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0969-6016(94)90010-8))
- MAIRCA ([ Colab Demo ](https://colab.research.google.com/drive/1gfqgrBAFGVygwm1j3lTjfy5wTsLgT_j5?usp=sharing)) ( [ Paper ](https://doi.org/10.1080/1331677X.2018.1506706))
- MARA ([ Colab Demo ](https://colab.research.google.com/drive/1Ggg5e7TKVF_JN4yZRq9zThJO-PRBSI-N?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/systems10060248))
- MARCOS ([ Colab Demo ](https://colab.research.google.com/drive/13MI2Qrakm5VzHN3r5O2RqggCzQwRxCs-?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.cie.2019.106231))
- MAUT ([ Colab Demo ](https://colab.research.google.com/drive/1qm3ARgQm68GUK2irGiCB-B49vnVHazB7?usp=sharing)) ( [ Paper ](https://doi.org/10.1017/CBO9781139174084))
- MEREC ([ Colab Demo ](https://colab.research.google.com/drive/1XE3AIzS84w-gw_1MEtV7xvkU1Gj_tRPd?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/sym13040525))
- Fuzzy MEREC ([ Colab Demo ](https://colab.research.google.com/drive/1yJ1eOXoGNp3amhoyBtEFCXr5PqwI9S5T?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/math11061544))
- MOORA ([ Colab Demo ](https://colab.research.google.com/drive/1FpKl0QAdwGgCVvLYsRHvMWhz7yOp17B5?usp=sharing)) ( [ Paper ](http://matwbn.icm.edu.pl/ksiazki/cc/cc35/cc35213.pdf))
- Fuzzy MOORA ([ Colab Demo ](https://colab.research.google.com/drive/1ydHzGeA8WBVY5Gyu8K7Oq6kofQ5XbK3P?usp=sharing)) ( [ Paper ](https://pdfs.semanticscholar.org/6d33/ca3f14c9ed44d23742fd4e9cf94cebcaf148.pdf))
- MOOSRA ([ Colab Demo ](https://colab.research.google.com/drive/1KYyA4f3OsipPA5e63Ja4A0OGmHvNY6dj?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.15623/ijret.2014.0315105))
- MULTIMOORA ([ Colab Demo ](https://colab.research.google.com/drive/1JAT8qqHPNoFfMV6a-CzF6BgRwtcUF3-e?usp=sharing)) ( [ Paper ](https://journals.vilniustech.lt/index.php/TEDE/article/view/5832/5078))
- OCRA ([ Colab Demo ](https://colab.research.google.com/drive/1yQ41lOdjhiANtD1SOXoxA7gVim7A4X4P?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.5937/sjm10-6802))
- Fuzzy OCRA ([ Colab Demo ](https://colab.research.google.com/drive/1SniY4RLsR6jR9SnI3AR9k0wGlBWH6Pm8?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.5755/j01.ee.30.5.20546))
- OPA ([ Colab Demo ](https://colab.research.google.com/drive/1RjryznPElHZUTuXQ2-ZKwJqMAVfJWKDC?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.asoc.2019.105893))
- ORESTE ([ Colab Demo ](https://colab.research.google.com/drive/1USVCt6KJHJK9NXaknY8wTA4L0d4r2kWw?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0377-2217(82)90131-X))
- PIV ([ Colab Demo ](https://colab.research.google.com/drive/1PwJoBqYn1O2s22MqC9euP89Uyv4sedS0?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.cie.2018.03.045))
- PROMETHEE I ([ Colab Demo ](https://colab.research.google.com/drive/1WsagC7-Y_5X-Xl90pMz8YwUkKfxf2vol?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))
- PROMETHEE II ([ Colab Demo ](https://colab.research.google.com/drive/143TUtTBy9y6gW0kMVAfhANBhuw1bKvBB?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))
- PROMETHEE III ([ Colab Demo ](https://colab.research.google.com/drive/11DBaEBBT8B-B3poXubvZ41HELOHok0Rz?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-3-030-15009-9_5
))
- PROMETHEE IV ([ Colab Demo ](https://colab.research.google.com/drive/1X2evE6pIf4F7qiKjt1fSU2PqT-NaA5sJ?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-3-319-11949-6_14))
- PROMETHEE V ([ Colab Demo ](https://colab.research.google.com/drive/1IaZCCtq5m8vBBxrBLMCp6xB5U2j8ZNRc?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))
- PROMETHEE VI ([ Colab Demo ](https://colab.research.google.com/drive/14QdhifGitj4GK-QijRr1vj_dmGU2Pfh4?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))
- PROMETHEE Gaia ([ Colab Demo ](https://colab.research.google.com/drive/1lj7IRKXcuRjrpoBp_KmQn_3sI3P_Qxju?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))
- EC PROMETHEE ([ Colab Demo ](https://colab.research.google.com/drive/1YxXXuc2urj7_sUreZAROFldAhE0o6gio?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/math11214432))
- PSI ([ Colab Demo ](https://colab.research.google.com/drive/1u9tN8cYl2mx6KK6yLW2oz6fuVoy8xcCI?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.matdes.2009.11.020))
- MPSI ([ Colab Demo ](https://colab.research.google.com/drive/1zj2AS6W_VWmG5mYgY4b-dnCK0q3AG1-K?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/systems10060248))
- RAFSI ([ Colab Demo ](https://colab.research.google.com/drive/13N85L87uh3wJXyja0zYvNglSju3-xVxU?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.3390/math8061015))
- REGIME ([ Colab Demo ](https://colab.research.google.com/drive/1jcAcjAS92rxvE2urhc6HPixvzJ60HqEg?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/BF00221383))
- ROC ([ Colab Demo ](https://colab.research.google.com/drive/1uUFXlCsZkFnh8HemNJ_hppvDC1eAwu4W?usp=sharing)) ( [ Paper ](https://doi.org/10.1002/(SICI)1099-0771(199806)11:2<85::AID-BDM282>3.0.CO;2-K))
- ROV ([ Colab Demo ](https://colab.research.google.com/drive/1sQAPCem0pcS29uf6-n4TpncXMXNx9JDh?usp=sharing)) ( [ Paper ](https://doi.org/10.5267/j.dsl.2015.12.001))
- RRW ([ Colab Demo ](https://colab.research.google.com/drive/1Pd13mNOosg0bxKAhALA3U9ppQYMB6yp_?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0030-5073(81)90015-5))
- RSW ([ Colab Demo ](https://colab.research.google.com/drive/1IvAmwypsA6J3JRKGQsi0fmwnlGmL3rKM?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0030-5073(81)90015-5))
- SAW ([ Colab Demo ](https://colab.research.google.com/drive/1R4cIsu0jBP9-6zwww_bNxEEnVGrhnS2d?usp=sharing)) ( [ Paper ](https://media.neliti.com/media/publications/326766-simple-additive-weighting-saw-method-in-f8f093e8.pdf))
- SECA ([ Colab Demo ](https://colab.research.google.com/drive/1Hs2zeOPJdkpdeXnfg6_GeWN6zo5JrIzn?usp=sharing)) ( [ Paper ](https://doi.org/10.15388/Informatica.2018.167))
- SMART ([ Colab Demo ](https://colab.research.google.com/drive/1K93HXHBR_v2da95Hh_CB6AmTCqta-k3D?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/978-1-4612-3982-6_4))
- SPOTIS ([ Colab Demo ](https://colab.research.google.com/drive/1TyjDn-xwut3w6Rf0zMiugdwytwmGY_NE?usp=sharing)) ( [ Paper ](https://doi.org/10.23919/FUSION45008.2020.9190347))
- TODIM ([ Colab Demo ](https://colab.research.google.com/drive/1EQqhhBQHHb8HT0TfuuVeFA2kwezsQYT1?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.ejor.2007.10.046))
- TOPSIS ([ Colab Demo ](https://colab.research.google.com/drive/1s87DC5_oa9GvgVe98oAP1UIhduac09CB?usp=sharing)) ( [ Paper ](https://doi.org/10.1057/jors.1987.44))
- Fuzzy TOPSIS ([ Colab Demo ](https://colab.research.google.com/drive/1eKx7AOYrnG-kZcsBt28rMEtCrUO-j3J-?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.procs.2016.07.088))
- VIKOR ([ Colab Demo ](https://colab.research.google.com/drive/1egZiTNvI2eE-tyJ2m85MM6B3-qhiSjPG?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/S0377-2217(03)00020-1))
- Fuzzy VIKOR ([ Colab Demo ](https://colab.research.google.com/drive/1anfCnU2TSrW-Z5vMkS_qXFrYZ0ciQE53?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.eswa.2011.04.097))
- WINGS ([ Colab Demo ](https://colab.research.google.com/drive/1li1_cPxwEM3NOZ4hbI8RROXyOmXeoWew?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.ejor.2013.02.007))
- WISP, Simple WISP ([ Colab Demo ](https://colab.research.google.com/drive/1xyJf3aydLdVPqhWpNyVXXD8T4PsrX0du?usp=sharing)) ( [ Paper ](https://doi.org/10.1109/TEM.2021.3075783))
- WSM, WPM, WASPAS ([ Colab Demo ](https://colab.research.google.com/drive/1HbLwXI4HkrmI-lsNzDtBOlCiwxfJltHi?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.acme.2013.07.006))
- Fuzzy WSM, Fuzzy WPM, Fuzzy WASPAS ([ Colab Demo ](https://colab.research.google.com/drive/1PcN_PaXwPHawzCU05UiHE504SkgF6vQ2?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.15837/ijccc.2015.6.2078))
4. Compare Methods:
- Compare Ranks & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/1RfLNEJjaHjtn3Lb2cfEDqS-iblaC4GQZ?usp=sharing))
- Compare Fuzzy Ranks & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/1pRO-E9xnk6DYEj_0DaEHUrUiCeIjmnXx?usp=sharing))
- Compare Weights & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/169hTJxP2APHrDA1h0fD1YEeu9s29wu0T?usp=sharing))
- Compare Fuzzy Weights & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/1nWDF8lrTmXlc-TE4_X1-MPhraFjytj1Z?usp=sharing))
5. Advanced MCDA Methods:
- [3MOAHP](https://github.com/Valdecy/Method_3MOAHP) - Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods
- [pyMissingAHP](https://github.com/Valdecy/pyMissingAHP) - A Method to Infer AHP Missing Pairwise Comparisons
- [ELECTRE-Tree](https://github.com/Valdecy/ELECTRE-Tree) - Algorithm to infer the ELECTRE Tri-B method parameters
- [Ranking-Trees](https://github.com/Valdecy/Ranking-Trees) - Algorithm to infer the ELECTRE II, III, IV, and PROMETHEE I, II, III, IV method parameters
# Acknowledgement
This section is dedicated to everyone who helped improve or correct the code. Thank you very much!
* Sabir Mohammedi Taieb (23.JANUARY.2023) - https://sabir97.github.io/ - Université Abdelhamid Ibn Badis Mostaganem (Algeria)
Raw data
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"author": "Valdecy Pereira",
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"description": "# pyDecision\n\n## Introduction\n\nA python library with the following MCDA methods: **AHP** (Analytic Hierarchy Process); **Fuzzy AHP**; **PPF AHP** (Proportional Picture Fuzzy AHP); **ARAS** (Additive Ratio ASsessment); **Fuzzy ARAS**; **Borda**; **BWM** (Best-Worst Method); **Simplified BWM**; **Fuzzy BWM**; **CILOS** (Criterion Impact LOSs); **CoCoSo** (COmbined COmpromise SOlution); **CODAS** (Combinative Distance-based Assessment); **Copeland**; **COPRAS** (Complex PRoportional Assessment); **Fuzzy COPRAS**; **CRADIS** (Compromise Ranking of Alternatives from Distance to Ideal Solution); **CRITIC** (CRiteria Importance Through Intercriteria Correlation); **Fuzzy CRITIC**; **DEMATEL** (DEcision MAking Trial and Evaluation Laboratory); **Fuzzy DEMATEL**; **EDAS** (Evaluation based on Distance from Average Solution); **Fuzzy EDAS**; **Entropy**; **ELECTRE** (I, I_s, I_v, II, III, IV, Tri-B); **FUCOM** (Full Consistency Method); **Fuzzy FUCOM**; **GRA** (Grey Relational Analysis); **IDOCRIW** (Integrated Determination of Objective CRIteria Weights); **MABAC** (Multi-Attributive Border Approximation area Comparison); **MACBETH** (Measuring Attractiveness by a Categorical Based Evaluation TecHnique); **MAIRCA** (Multi-Attributive Ideal-Real Comparative Analysis); **MARA** (Magnitude of the Area for the Ranking of Alternatives) ; **MARCOS** (Measurement of Alternatives and Ranking according to COmpromise Solution); **MAUT** (Multi-attribute Utility Theory); **MEREC** (MEthod based on the Removal Effects of Criteria); **MOORA** (Multi-Objective Optimization on the basis of Ratio Analysis); **Fuzzy MOORA**; **MOOSRA** (Multi-Objective Optimisation on the Basis of Simple Ratio Analysis); **MULTIMOORA** (Multi-Objective Optimization on the basis of Ratio Analisys Multiplicative Form); **OCRA** (Operational Competitiveness RAting); **Fuzzy OCRA** ; **OPA** (Ordinal Priority Approach); **ORESTE** (Organisation Rangement Et SynThesE de donnees relationnelles); **PIV** (Proximity Indexed Value); **PROMETHEE** (I, II, III, IV, V, VI, Gaia); **EC PROMETHEE**; **RAFSI** (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval); **REGIME** (REGIonal Multicriteria Elimination); **ROC** (Rank Ordered Centroid); **ROV** (Range Of Value); **RRW** (Rank Reciprocal Weighting); **RSW** (Rank Summed Weight); **SAW** (Simple Additive Weighting); **SECA** (Simultaneous Evaluation of Criteria and Alternatives); **SMART** (Simple Multi-Attribute Rating Technique); **SPOTIS** (Stable Preference Ordering Towards Ideal Solution); **TODIM** (TOmada de Decisao Interativa e Multicriterio - Interactive and Multicriteria Decision Making); **PSI** (Preference Selection Index); **MPSI** (Modified Preference Selection Index); **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); **WISP** (Integrated Simple Weighted Sum Product); **Simple WISP**; **WSM** (Weighted Sum Model); **Fuzzy WSM**; **WPM** (Weighted Product Model); **Fuzzy WPM**; **WASPAS** (Weighted Aggregates Sum Product Assessment); **Fuzzy WASPAS**. \n\npyDecision offers an array of features, including the **comparison of ranking alternatives** and **comparison of criterion weights** from various methods. The library is also fully integrated with **chatGPT**, elevating result interpretation through AI. Additionally, pyDecision provides the flexibility to import results from custom methods or those not yet implemented in the library for swift comparison.\n\n## Citation\n\nPEREIRA, V.; BASILIO, M.P.; SANTOS, C.H.T (2024). Enhancing Decision Analysis with a Large Language Model: pyDecision a Comprehensive Library of MCDA Methods in Python. arXiv. https://arxiv.org/abs/2404.06370\n\n## Usage\n\n1. Install\n```bash\npip install pyDecision\n```\n\n2. Import\n\n```py3\n\n# Import AHP\nfrom pyDecision.algorithm import ahp_method\n\n# Parameters\nweight_derivation = 'geometric' # 'mean'; 'geometric' or 'max_eigen'\n\n# Dataset\ndataset = np.array([\n #g1 g2 g3 g4 g5 g6 g7 \n [1 , 1/3, 1/5, 1 , 1/4, 1/2, 3 ], #g1\n [3 , 1 , 1/2, 2 , 1/3, 3 , 3 ], #g2\n [5 , 2 , 1 , 4 , 5 , 6 , 5 ], #g3\n [1 , 1/2, 1/4, 1 , 1/4, 1 , 2 ], #g4\n [4 , 3 , 1/5, 4 , 1 , 3 , 2 ], #g5\n [2 , 1/3, 1/6, 1 , 1/3, 1 , 1/3], #g6\n [1/3, 1/3, 1/5, 1/2, 1/2, 3 , 1 ] #g7\n])\n\n# Call AHP Function\nweights, rc = ahp_method(dataset, wd = weight_derivation)\n\n# Weigths\nfor i in range(0, weights.shape[0]):\n print('w(g'+str(i+1)+'): ', round(weights[i], 3))\n \n# Consistency Ratio\nprint('RC: ' + str(round(rc, 2)))\nif (rc > 0.10):\n print('The solution is inconsistent, the pairwise comparisons must be reviewed')\nelse:\n print('The solution is consistent')\n\n```\n\n3. Try it in **Colab**:\n\n- AHP ([ Colab Demo ](https://colab.research.google.com/drive/1qwFQs5xkTZ8K-Ul_wWcCtPjLH0QooU9g?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1016/0377-2217(90)90057-I))\n- Fuzzy AHP ([ Colab Demo ](https://colab.research.google.com/drive/1RtEMOLGL5wtmheMRZv8emcO5wbjYVBCo?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/S0165-0114(83)80082-7))\n- PPF AHP ([ Colab Demo ](https://colab.research.google.com/drive/1wI-8z2aysGKhSI3PLxN6vOZ4jsbPmKYl?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.eswa.2023.122354))\n- ARAS ([ Colab Demo ](https://colab.research.google.com/drive/1rwQgXjvC3E6pRhOs7CkcCV8Vw2bXEPLy?usp=sharing)) ( [ Paper ](https://doi.org/10.3846/tede.2010.10))\n- Fuzzy ARAS ([ Colab Demo ](https://colab.research.google.com/drive/1kZDkEWsw0d0nFhDQQk8azZXRod7RnfZr?usp=sharing)) ( [ Paper ](https://doi.org/10.3846/transport.2010.52))\n- Borda ([ Colab Demo ](https://colab.research.google.com/drive/1t5RVtG7_yXK-nPxM0MVd4U01qfTQYW4k?usp=sharing)) ( [ Paper ](http://gerardgreco.free.fr/IMG/pdf/MA_c_moire-Borda-1781.pdf))\n- BWM ([ Colab Demo ](https://colab.research.google.com/drive/1XkacTmtSBvZmx_5K9cfz8t1Ao5j-D-bZ?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.omega.2014.11.009))\n- Simplified BWM ([ Colab Demo ](https://colab.research.google.com/drive/1v3QfSdprM8gwxL4VWmh75mPiPn2YWOZn?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/su13084487))\n- Fuzzy BWM ([ Colab Demo ](https://colab.research.google.com/drive/1hBTXyOLpBoC7oE-hsolPH2O0ekzp4VU0?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.knosys.2017.01.010))\n- CILOS ([ Colab Demo ](https://colab.research.google.com/drive/1RnSqO_VEPyvXAMHdneloYvA0TzPx55kw?usp=sharing)) ( [ Paper ](https://doi.org/10.1142/S0219622016500036))\n- CoCoSo ([ Colab Demo ](https://colab.research.google.com/drive/1U8a3NZzQaxDkJdUT3uKIeeoqFtT_3Mnx?usp=sharing)) ( [ Paper ](https://doi.org/10.1108/MD-05-2017-0458))\n- CODAS ([ Colab Demo ](https://colab.research.google.com/drive/1hm7__urqFeBHM6nVQJcBzGPF72DFuoLr?usp=sharing)) ( [ Paper ](https://ideas.repec.org/a/cys/ecocyb/v50y2016i3p25-44.html))\n- Copeland ([ Colab Demo ](https://colab.research.google.com/drive/1ObP3AkQAzoCxT6et5Qkyk1trlER7mcdH?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/BF01212012))\n- COPRAS ([ Colab Demo ](https://colab.research.google.com/drive/1TZJtSjXqwYEwuL7-wfLcPQ8ZBtDq3lth?usp=sharing)) ( [ Paper ](https://doi.org/10.3846/20294913.2012.762953))\n- Fuzzy COPRAS ([ Colab Demo ](https://colab.research.google.com/drive/1AIGgxBkmcA6YHKx06VeYcGf2EV8dPffW?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/s00500-021-05762-w))\n- CRADIS ([ Colab Demo ](https://colab.research.google.com/drive/1p7AQmPIOsZFxaypqMsiRIWW8mIvDtoLi?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/s10668-021-01902-2))\n- CRITIC ([ Colab Demo ](https://colab.research.google.com/drive/1D5SaBHa1-Eo_KYSXHkFjsHYu29M21l_F?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0305-0548(94)00059-H))\n- Fuzzy CRITIC ([ Colab Demo ](https://colab.research.google.com/drive/1wofFhWDw6fn-XpLQoQjveKjyaId-AsEw?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.engappai.2022.104942))\n- DEMATEL ([ Colab Demo ](https://colab.research.google.com/drive/1T04qEft9uwTyQx--gADN6V_vUrT21Xo6?usp=sharing)) ( [ Paper ](https://doi.org/10.1155/2018/3696457))\n- Fuzzy DEMATEL ([ Colab Demo ](https://colab.research.google.com/drive/15e9dMDROr3cxjbWRXg3_t4TScuQtQDpR?usp=sharing)) ( [ Paper ](https://www.sciencedirect.com/science/article/abs/pii/S0957417405003593))\n- EDAS ([ Colab Demo ](https://colab.research.google.com/drive/1xsMdwH-IH-zvOW-1kv6ztQnKGt7p5JnY?usp=sharing)) ( [ Paper ](https://doi.org/10.15388/Informatica.2015.57))\n- Fuzzy EDAS ([ Colab Demo ](https://colab.research.google.com/drive/1kw2LwztNAU9Asjj6BvBmvk11wvk8R3V6?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/978-981-32-9072-3_63))\n- Entropy ([ Colab Demo ](https://colab.research.google.com/drive/1LOCef2KFxoV2qUEQRi4DqfzrgnMgtwT9?usp=sharing)) ( [ Paper ](https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf))\n- ELECTRE I ([ Colab Demo ](https://colab.research.google.com/drive/1KFqRPBRyv-fxiu2B1y7VNkP5pCCbILF1?usp=sharing)) ( [ Paper ](https://github.com/Valdecy/Datasets/blob/master/MCDA/E01.pdf))\n- ELECTRE I_s ([ Colab Demo ](https://colab.research.google.com/drive/1ngxsQPh2QULjd1_AifFofbukq5zIOePd?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))\n- ELECTRE I_v ([ Colab Demo ](https://colab.research.google.com/drive/1moonq95gqXqmbRe2KvgqbN2IfowJ12C-?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))\n- ELECTRE II ([ Colab Demo ](https://colab.research.google.com/drive/1UeAjICH6_tjVr3O9H-fC65HHYMVZgTKc?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))\n- ELECTRE III ([ Colab Demo ](https://colab.research.google.com/drive/1smeD5ZoPgBnAAUyooAXSrkxHgqZPmUC9?usp=sharing)) ( [ Paper ](https://github.com/Valdecy/Datasets/raw/master/MCDA/E03.pdf))\n- ELECTRE IV ([ Colab Demo ](https://colab.research.google.com/drive/178x062yC-Es6lstEiFaFprbMsTJZwnC-?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-1-4757-5057-7_3))\n- ELECTRE Tri-B ([ Colab Demo ](https://colab.research.google.com/drive/1hu0fJcxdBAiEDrVngmKQfpINpjTF-osE?usp=sharing)) ( [ Paper ](https://drive.google.com/file/d/1oWOI_sX3EEYdRbavoBTT7vUmPII1yPgE/view?usp=sharing))\n- FUCOM ([ Colab Demo ](https://colab.research.google.com/drive/1eWP3xf3-9iLLW_l_9JuAe6BEeoMsqzcL?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/sym10090393))\n- Fuzzy FUCOM ([ Colab Demo ](https://colab.research.google.com/drive/1bkelWth_7TOW_gIz8mBNe_4W5Ox84FUB?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/su14094972 ))\n- GRA ([ Colab Demo ](https://colab.research.google.com/drive/1aMMI0Cuo5kpzTDefqEwJhf0wWpBOP_JL?usp=sharing)) ( [ Paper ](https://uranos.ch/research/references/Julong_1989/10.1.1.678.3477.pdf))\n- IDOCRIW ([ Colab Demo ](https://colab.research.google.com/drive/1zt8uPFZGcHaSnpiT7tDnrDjvs0pK_7vS?usp=sharing)) ( [ Paper ](https://doi.org/10.1142/S0219622016500036))\n- MABAC ([ Colab Demo ](https://colab.research.google.com/drive/1BMqO-HnBXdcOZfZoULpx1H4MLPoUGucJ?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.eswa.2014.11.057))\n- MACBETH ([ Colab Demo ](https://colab.research.google.com/drive/1GqM9uPgbaWCGyj4l-XjkoifY2JJoVyf2?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0969-6016(94)90010-8))\n- MAIRCA ([ Colab Demo ](https://colab.research.google.com/drive/1gfqgrBAFGVygwm1j3lTjfy5wTsLgT_j5?usp=sharing)) ( [ Paper ](https://doi.org/10.1080/1331677X.2018.1506706))\n- MARA ([ Colab Demo ](https://colab.research.google.com/drive/1Ggg5e7TKVF_JN4yZRq9zThJO-PRBSI-N?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/systems10060248))\n- MARCOS ([ Colab Demo ](https://colab.research.google.com/drive/13MI2Qrakm5VzHN3r5O2RqggCzQwRxCs-?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.cie.2019.106231))\n- MAUT ([ Colab Demo ](https://colab.research.google.com/drive/1qm3ARgQm68GUK2irGiCB-B49vnVHazB7?usp=sharing)) ( [ Paper ](https://doi.org/10.1017/CBO9781139174084))\n- MEREC ([ Colab Demo ](https://colab.research.google.com/drive/1XE3AIzS84w-gw_1MEtV7xvkU1Gj_tRPd?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/sym13040525))\n- Fuzzy MEREC ([ Colab Demo ](https://colab.research.google.com/drive/1yJ1eOXoGNp3amhoyBtEFCXr5PqwI9S5T?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/math11061544))\n- MOORA ([ Colab Demo ](https://colab.research.google.com/drive/1FpKl0QAdwGgCVvLYsRHvMWhz7yOp17B5?usp=sharing)) ( [ Paper ](http://matwbn.icm.edu.pl/ksiazki/cc/cc35/cc35213.pdf))\n- Fuzzy MOORA ([ Colab Demo ](https://colab.research.google.com/drive/1ydHzGeA8WBVY5Gyu8K7Oq6kofQ5XbK3P?usp=sharing)) ( [ Paper ](https://pdfs.semanticscholar.org/6d33/ca3f14c9ed44d23742fd4e9cf94cebcaf148.pdf))\n- MOOSRA ([ Colab Demo ](https://colab.research.google.com/drive/1KYyA4f3OsipPA5e63Ja4A0OGmHvNY6dj?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.15623/ijret.2014.0315105))\n- MULTIMOORA ([ Colab Demo ](https://colab.research.google.com/drive/1JAT8qqHPNoFfMV6a-CzF6BgRwtcUF3-e?usp=sharing)) ( [ Paper ](https://journals.vilniustech.lt/index.php/TEDE/article/view/5832/5078))\n- OCRA ([ Colab Demo ](https://colab.research.google.com/drive/1yQ41lOdjhiANtD1SOXoxA7gVim7A4X4P?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.5937/sjm10-6802))\n- Fuzzy OCRA ([ Colab Demo ](https://colab.research.google.com/drive/1SniY4RLsR6jR9SnI3AR9k0wGlBWH6Pm8?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.5755/j01.ee.30.5.20546))\n- OPA ([ Colab Demo ](https://colab.research.google.com/drive/1RjryznPElHZUTuXQ2-ZKwJqMAVfJWKDC?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.asoc.2019.105893))\n- ORESTE ([ Colab Demo ](https://colab.research.google.com/drive/1USVCt6KJHJK9NXaknY8wTA4L0d4r2kWw?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0377-2217(82)90131-X))\n- PIV ([ Colab Demo ](https://colab.research.google.com/drive/1PwJoBqYn1O2s22MqC9euP89Uyv4sedS0?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.cie.2018.03.045))\n- PROMETHEE I ([ Colab Demo ](https://colab.research.google.com/drive/1WsagC7-Y_5X-Xl90pMz8YwUkKfxf2vol?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))\n- PROMETHEE II ([ Colab Demo ](https://colab.research.google.com/drive/143TUtTBy9y6gW0kMVAfhANBhuw1bKvBB?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))\n- PROMETHEE III ([ Colab Demo ](https://colab.research.google.com/drive/11DBaEBBT8B-B3poXubvZ41HELOHok0Rz?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-3-030-15009-9_5\n))\n- PROMETHEE IV ([ Colab Demo ](https://colab.research.google.com/drive/1X2evE6pIf4F7qiKjt1fSU2PqT-NaA5sJ?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.1007/978-3-319-11949-6_14))\n- PROMETHEE V ([ Colab Demo ](https://colab.research.google.com/drive/1IaZCCtq5m8vBBxrBLMCp6xB5U2j8ZNRc?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))\n- PROMETHEE VI ([ Colab Demo ](https://colab.research.google.com/drive/14QdhifGitj4GK-QijRr1vj_dmGU2Pfh4?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))\n- PROMETHEE Gaia ([ Colab Demo ](https://colab.research.google.com/drive/1lj7IRKXcuRjrpoBp_KmQn_3sI3P_Qxju?usp=sharing)) ( [ Paper ](https://www.cin.ufpe.br/~if703/aulas/promethee.pdf))\n- EC PROMETHEE ([ Colab Demo ](https://colab.research.google.com/drive/1YxXXuc2urj7_sUreZAROFldAhE0o6gio?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/math11214432))\n- PSI ([ Colab Demo ](https://colab.research.google.com/drive/1u9tN8cYl2mx6KK6yLW2oz6fuVoy8xcCI?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.matdes.2009.11.020))\n- MPSI ([ Colab Demo ](https://colab.research.google.com/drive/1zj2AS6W_VWmG5mYgY4b-dnCK0q3AG1-K?usp=sharing)) ( [ Paper ](https://doi.org/10.3390/systems10060248))\n- RAFSI ([ Colab Demo ](https://colab.research.google.com/drive/13N85L87uh3wJXyja0zYvNglSju3-xVxU?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.3390/math8061015))\n- REGIME ([ Colab Demo ](https://colab.research.google.com/drive/1jcAcjAS92rxvE2urhc6HPixvzJ60HqEg?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/BF00221383))\n- ROC ([ Colab Demo ](https://colab.research.google.com/drive/1uUFXlCsZkFnh8HemNJ_hppvDC1eAwu4W?usp=sharing)) ( [ Paper ](https://doi.org/10.1002/(SICI)1099-0771(199806)11:2<85::AID-BDM282>3.0.CO;2-K))\n- ROV ([ Colab Demo ](https://colab.research.google.com/drive/1sQAPCem0pcS29uf6-n4TpncXMXNx9JDh?usp=sharing)) ( [ Paper ](https://doi.org/10.5267/j.dsl.2015.12.001))\n- RRW ([ Colab Demo ](https://colab.research.google.com/drive/1Pd13mNOosg0bxKAhALA3U9ppQYMB6yp_?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0030-5073(81)90015-5))\n- RSW ([ Colab Demo ](https://colab.research.google.com/drive/1IvAmwypsA6J3JRKGQsi0fmwnlGmL3rKM?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/0030-5073(81)90015-5))\n- SAW ([ Colab Demo ](https://colab.research.google.com/drive/1R4cIsu0jBP9-6zwww_bNxEEnVGrhnS2d?usp=sharing)) ( [ Paper ](https://media.neliti.com/media/publications/326766-simple-additive-weighting-saw-method-in-f8f093e8.pdf))\n- SECA ([ Colab Demo ](https://colab.research.google.com/drive/1Hs2zeOPJdkpdeXnfg6_GeWN6zo5JrIzn?usp=sharing)) ( [ Paper ](https://doi.org/10.15388/Informatica.2018.167))\n- SMART ([ Colab Demo ](https://colab.research.google.com/drive/1K93HXHBR_v2da95Hh_CB6AmTCqta-k3D?usp=sharing)) ( [ Paper ](https://doi.org/10.1007/978-1-4612-3982-6_4))\n- SPOTIS ([ Colab Demo ](https://colab.research.google.com/drive/1TyjDn-xwut3w6Rf0zMiugdwytwmGY_NE?usp=sharing)) ( [ Paper ](https://doi.org/10.23919/FUSION45008.2020.9190347))\n- TODIM ([ Colab Demo ](https://colab.research.google.com/drive/1EQqhhBQHHb8HT0TfuuVeFA2kwezsQYT1?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.ejor.2007.10.046))\n- TOPSIS ([ Colab Demo ](https://colab.research.google.com/drive/1s87DC5_oa9GvgVe98oAP1UIhduac09CB?usp=sharing)) ( [ Paper ](https://doi.org/10.1057/jors.1987.44))\n- Fuzzy TOPSIS ([ Colab Demo ](https://colab.research.google.com/drive/1eKx7AOYrnG-kZcsBt28rMEtCrUO-j3J-?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.procs.2016.07.088))\n- VIKOR ([ Colab Demo ](https://colab.research.google.com/drive/1egZiTNvI2eE-tyJ2m85MM6B3-qhiSjPG?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/S0377-2217(03)00020-1))\n- Fuzzy VIKOR ([ Colab Demo ](https://colab.research.google.com/drive/1anfCnU2TSrW-Z5vMkS_qXFrYZ0ciQE53?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.eswa.2011.04.097))\n- WINGS ([ Colab Demo ](https://colab.research.google.com/drive/1li1_cPxwEM3NOZ4hbI8RROXyOmXeoWew?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.ejor.2013.02.007))\n- WISP, Simple WISP ([ Colab Demo ](https://colab.research.google.com/drive/1xyJf3aydLdVPqhWpNyVXXD8T4PsrX0du?usp=sharing)) ( [ Paper ](https://doi.org/10.1109/TEM.2021.3075783))\n- WSM, WPM, WASPAS ([ Colab Demo ](https://colab.research.google.com/drive/1HbLwXI4HkrmI-lsNzDtBOlCiwxfJltHi?usp=sharing)) ( [ Paper ](https://doi.org/10.1016/j.acme.2013.07.006))\n- Fuzzy WSM, Fuzzy WPM, Fuzzy WASPAS ([ Colab Demo ](https://colab.research.google.com/drive/1PcN_PaXwPHawzCU05UiHE504SkgF6vQ2?usp=sharing)) ( [ Paper ](http://dx.doi.org/10.15837/ijccc.2015.6.2078))\n\n4. Compare Methods:\n- Compare Ranks & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/1RfLNEJjaHjtn3Lb2cfEDqS-iblaC4GQZ?usp=sharing))\n- Compare Fuzzy Ranks & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/1pRO-E9xnk6DYEj_0DaEHUrUiCeIjmnXx?usp=sharing))\n- Compare Weights & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/169hTJxP2APHrDA1h0fD1YEeu9s29wu0T?usp=sharing))\n- Compare Fuzzy Weights & Ask chatGPT ([ Colab Demo ](https://colab.research.google.com/drive/1nWDF8lrTmXlc-TE4_X1-MPhraFjytj1Z?usp=sharing))\n\n5. Advanced MCDA Methods:\n\n- [3MOAHP](https://github.com/Valdecy/Method_3MOAHP) - Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods\n- [pyMissingAHP](https://github.com/Valdecy/pyMissingAHP) - A Method to Infer AHP Missing Pairwise Comparisons\n- [ELECTRE-Tree](https://github.com/Valdecy/ELECTRE-Tree) - Algorithm to infer the ELECTRE Tri-B method parameters\n- [Ranking-Trees](https://github.com/Valdecy/Ranking-Trees) - Algorithm to infer the ELECTRE II, III, IV, and PROMETHEE I, II, III, IV method parameters\n\n# Acknowledgement \n\nThis section is dedicated to everyone who helped improve or correct the code. Thank you very much!\n\n* Sabir Mohammedi Taieb (23.JANUARY.2023) - https://sabir97.github.io/ - Universit\u00c3\u00a9 Abdelhamid Ibn Badis Mostaganem (Algeria)\n",
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