objective_weighting.mcda_methods

Submodules

• objective_weighting.mcda_methods.mcda_method
• objective_weighting.mcda_methods.vikor
• objective_weighting.mcda_methods.vikor_smaa

Package Contents

Classes

VIKOR
VIKOR_SMAA

class objective_weighting.mcda_methods.VIKOR(normalization_method=None, v=0.5)

Bases: objective_weighting.mcda_methods.mcda_method.MCDA_method

__call__(self, matrix, weights, types)

Score alternatives provided in decision matrix matrix using criteria weights and criteria types.

Parameters

• matrix (ndarray) – Decision matrix with m alternatives in rows and n criteria in columns.

• weights (ndarray) – Matrix containing vectors with criteria weights in subsequent rows. Sum of weights in each vector must be equal to 1.

• types (ndarray) – Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

Matrix with vectors containing preference values of each alternative. The best alternative has the lowest preference value. Vectors are placed in subsequent columns of matrix.

Return type

ndrarray

Examples

>>> vikor = VIKOR(normalization_method = minmax_normalization)
>>> pref = vikor(matrix, weights, types)
>>> rank = np.zeros((pref.shape))
>>> for i in range(pref.shape[1]):
>>>     rank[:, i] = rank_preferences(pref[:, i], reverse = False)

static _vikor(matrix, weights, types, normalization_method, v)

class objective_weighting.mcda_methods.VIKOR_SMAA(normalization_method=None, v=0.5)

__call__(self, matrix, weights, types)

Score alternatives provided in decision matrix matrix using criteria weights and criteria types.

Parameters

• matrix (ndarray) – Decision matrix with m alternatives in rows and n criteria in columns.

• weights (ndarray) – Matrix with i vectors in rows of n weights in columns. i means number of iterations of SMAA

• types (ndarray) – Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

Matrix with acceptability indexes values for each alternative in rows in relation to each rank in columns, Matrix with central weight vectors for each alternative in rows Matrix with final ranking of alternatives

Return type

ndrarray, ndarray, ndarray

Examples

>>> vikor_smaa = VIKOR_SMAA(normalization_method = minmax_normalization)
>>> rank_acceptability_index, central_weight_vector, rank_scores = vikor_smaa(matrix, weights, types)

_generate_weights(self, n, iterations)

Function to generate multiple weight vectors

Parameters

• n (int) – Number of criteria

• iterations (int) – Number of weight vector to generate

Returns

Matrix containing in rows vectors with weights for n criteria

Return type

ndarray

static _vikor_smaa(self, matrix, weights, types, normalization_method, v)