About
PyMCDM
Python 3 library for solving multi-criteria decision-making (MCDM) problems.
Documentation is avaliable on readthedocs.
Installation
You can download and install pymcdm
library using pip:
pip install pymcdm
You can run all tests with following command from the root of the project:
python -m unittest -v
Citing pymcdm
If usage of the pymcdm library lead to a scientific publication, please acknowledge this fact by citing “Kizielewicz, B., Shekhovtsov, A., & Sałabun, W. (2023). pymcdm—The universal library for solving multi-criteria decision-making problems. SoftwareX, 22, 101368.”
Or using BibTex:
@article{kizielewicz2023pymcdm,
title={pymcdm—The universal library for solving multi-criteria decision-making problems},
author={Kizielewicz, Bart{\l}omiej and Shekhovtsov, Andrii and Sa{\l}abun, Wojciech},
journal={SoftwareX},
volume={22},
pages={101368},
year={2023},
publisher={Elsevier}
}
Available methods
The library contains:
MCDA methods:
Acronym |
Method Name |
Reference |
---|---|---|
TOPSIS |
Technique for the Order of Prioritisation by Similarity to Ideal Solution |
|
VIKOR |
VIseKriterijumska Optimizacija I Kompromisno Resenje |
|
COPRAS |
COmplex PRoportional ASsessment |
|
PROMETHEE I & II |
Preference Ranking Organization METHod for Enrichment of Evaluations I & II |
|
COMET |
Characteristic Objects Method |
|
SPOTIS |
Stable Preference Ordering Towards Ideal Solution |
|
ARAS |
Additive Ratio ASsessment |
|
COCOSO |
COmbined COmpromise SOlution |
|
CODAS |
COmbinative Distance-based ASsessment |
|
EDAS |
Evaluation based on Distance from Average Solution |
|
MABAC |
Multi-Attributive Border Approximation area Comparison |
|
MAIRCA |
MultiAttributive Ideal-Real Comparative Analysis |
|
MARCOS |
Measurement Alternatives and Ranking according to COmpromise Solution |
|
OCRA |
Operational Competitiveness Ratings |
|
MOORA |
Multi-Objective Optimization Method by Ratio Analysis |
|
RIM |
Reference Ideal Method |
|
ERVD |
Election Based on relative Value Distances |
|
PROBID |
Preference Ranking On the Basis of Ideal-average Distance |
|
WSM |
Weighted Sum Model |
|
WPM |
Weighted Product Model |
|
WASPAS |
Weighted Aggregated Sum Product ASSessment |
|
RAM |
Root Assesment Method |
Weighting methods:
Acronym |
Method Name |
Reference |
---|---|---|
- |
Equal/Mean weights |
|
- |
Entropy weights |
|
STD |
Standard Deviation weights |
|
MEREC |
MEthod based on the Removal Effects of Criteria |
|
CRITIC |
CRiteria Importance Through Intercriteria Correlation |
|
CILOS |
Criterion Impact LOS |
|
IDOCRIW |
Integrated Determination of Objective CRIteria Weight |
|
- |
Angular/Angle weights |
|
- |
Gini Coeficient weights |
|
- |
Statistical variance weights |
Normalization methods:
Method Name |
Reference |
---|---|
Weitendorf’s Linear Normalization |
|
Maximum - Linear Normalization |
|
Sum-Based Linear Normalization |
|
Vector Normalization |
|
Logarithmic Normalization |
|
Linear Normalization (Max-Min) |
|
Non-linear Normalization (Max-Min) |
|
Enhanced Accuracy Normalization |
|
Lai and Hwang Normalization |
|
Zavadskas and Turskis Normalization |
Correlation coefficients:
Coefficient name |
Reference |
---|---|
Spearman’s rank correlation coefficient |
|
Pearson correlation coefficient |
|
Weighted Spearman’s rank correlation coefficient |
|
Rank Similarity Coefficient |
|
Kendall rank correlation coefficient |
|
Goodman and Kruskal’s gamma |
|
Drastic Weighted Similarity (draWS) |
|
Weights Similarity Coefficient (WSC) |
|
Weights Similarity Coefficient 2 (WSC2) |
Helpers
Helpers submodule |
Description |
---|---|
|
Create ranking vector from the preference vector. Smaller preference values has higher positions in the ranking. |
|
Alias to the |
|
Create the correlation matrix for given coefficient from several the several rankings. |
|
Normalize decision matrix column by column using given normalization and criteria types. |
COMET Tools
Class/Function |
Description |
Reference |
---|---|---|
|
Class which allows to evaluate CO in COMET using any MCDA method. |
|
|
Class which allows to evaluate CO in COMET manually by pairwise comparisons. |
|
|
Class which allows to evaluate CO in COMET using any expert function. |
|
|
Class which allows to evaluate CO in COMET using compromise between several different methods. |
- |
|
Class which allows to evaluate CO in COMET manually but with triads support. |
In Press |
|
Class which allows to identify MEJ using expert-defined Expected Solution Points. |
|
|
Function to which evaluates consistency of the MEJ matrix. |
|
|
Class mostly for internal use in StructuralCOMET class. |
|
|
Class which allows to split a decision problem into submodels to be evaluated by the COMET method. |
Usage example
Here’s a small example of how use this library to solve MCDM problem. For more examples with explanation see examples.
import numpy as np
from pymcdm.methods import TOPSIS
from pymcdm.helpers import rrankdata
# Define decision matrix (2 criteria, 4 alternative)
alts = np.array([
[4, 4],
[1, 5],
[3, 2],
[4, 2]
], dtype='float')
# Define weights and types
weights = np.array([0.5, 0.5])
types = np.array([1, -1])
# Create object of the method
topsis = TOPSIS()
# Determine preferences and ranking for alternatives
pref = topsis(alts, weights, types)
ranking = rrankdata(pref)
for r, p in zip(ranking, pref):
print(r, p)
And the output of this example (numbers are rounded):
3 0.6126
4 0.0
2 0.7829
1 1.0
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