skcriteria.agg.waspas module
WASPAS method.
- skcriteria.agg.waspas.waspas(matrix, weights, lambda_value)[source]
Execute WASPAS without any validation.
- class skcriteria.agg.waspas.WASPAS(*, lambda_value=0.5)[source]
Bases:
SKCDecisionMakerABCThe Weighted Aggregated Sum Product ASsessment method.
WASPAS is a multicriteria decision analysis method that combines the Weighted Sum Model (WSM) and the Weighted Product Model (WPM) using an aggregation parameter \(\lambda \in [0, 1]\).
It is very important to state here that it is applicable only when all the data are expressed in exactly the same unit. If this is not the case, then the final result is equivalent to “adding apples and oranges”. To avoid this problem a previous normalization step is necessary.
In general, suppose that a given MCDA problem is defined on \(m\) alternatives and \(n\) decision criteria. Let \(w_j\) denote the weight of criterion \(C_j\), and \(a_{ij}\) be the performance value of alternative \(A_i\) with respect to criterion \(C_j\).
The WASPAS score of alternative \(A_i\) is defined as:
\[A_i^{WASPAS} = \lambda \cdot \sum_{j=1}^{n} w_j a_{ij} + (1 - \lambda) \cdot \prod_{j=1}^{n} a_{ij}^{w_j}\]By default, \(\lambda = 0.5\).
- Raises:
ValueError: – If some objective is for minimization, or some value in the matrix is <= 0, or if the parameter lambda_value is not in the range [0, 1].
References
- property lambda_value
Aggregation parameter in [0, 1] that balances WSM and WPM.