objective-weighting 0.1.0

The Python 3 Library of Objective Weighting Techniques for MCDA methods.

objective-weighting

The Python 3 Library of Objective Weighting Techniques for MCDA methods.

Installation

pip install objective-weighting

Usage

objective-weighting is the Python 3 package that provides 11 objective weighting methods, which can be used to determine criteria weights for solving multi-criteria problems with Multi-Criteria Decision Analysis (MCDA) methods. The first step is providing the decision matrix matrix with alternatives performance values. The decision matrix is two-dimensional and contains m alternatives in rows and n criteria in columns. You also have to provide criteria types types. Criteria types are equal to 1 for profit criteria and -1 for cost criteria. Then you have to calculate criteria weights using chosen from weighting_methods module weighting method. Depending on the method chosen, you have to provide matrix or matrix and types as weighting method arguments. It is detailed in Usage in the documentation. Then you can evaluate alternatives from the decision matrix using the VIKOR method from mcda_methods module. The VIKOR method returns a vector with preference values pref assigned to alternatives. To rank alternatives according to VIKOR preference values, you have to sort them in ascending order because, in the VIKOR method, the best alternative has the lowest preference value. The alternatives are ranked using the rank_preferences method provided in the additions module of the objective-weighting package. Parameter reverse = False means that alternatives are sorted in ascending order. Here is an example of using the Entropy weighting method entropy_weighting for determining criteria weights and the VIKOR method to calculate preference values:

import numpy as np
from objective_weighting.mcda_methods import VIKOR
from objective_weighting import weighting_methods as mcda_weights
from objective_weighting import normalizations as norms
from objective_weighting.additions import rank_preferences

matrix = np.array([[256, 8, 41, 1.6, 1.77, 7347.16],
[256, 8, 32, 1.0, 1.8, 6919.99],
[256, 8, 53, 1.6, 1.9, 8400],
[256, 8, 41, 1.0, 1.75, 6808.9],
[512, 8, 35, 1.6, 1.7, 8479.99],
[256, 4, 35, 1.6, 1.7, 7499.99]])

types = np.array([1, 1, 1, 1, -1, -1])
weights = mcda_weights.entropy_weighting(matrix)

# Create the VIKOR method object
vikor = VIKOR(normalization_method=norms.minmax_normalization)
# Calculate alternatives preference function values with VIKOR method
pref = vikor(matrix, weights, types)
# Rank alternatives according to preference values
rank = rank_preferences(pref, reverse = False)

License

objective-weighting was created by Aleksandra Bączkiewicz. It is licensed under the terms of the MIT license.

Documentation

Documentation of this library with instruction for installation and usage is provided here