Methods based on an aggregating function representing “closeness to the ideal”.
TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the ideal solution and the longest euclidean distance from the worst solution.
An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing, and also allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion.
mnorm : string, callable, optional (default=”vector”)
Normalization method for the alternative matrix.
wnorm : string, callable, optional (default=”sum”)
Normalization method for the weights array.
- kernel_: None
- rank_: A ranking (start at 1) where the i-nth element represent the position of the i-nth alternative.
- best_alternative_: The index of the best alternative.
- alpha_solution_: True
- beta_solution_: False
- gamma_solution_: True
- e_: Particular data created by this method.
- e_.closeness: Array where the i-nth element represent the closenees of the i-nth alternative to ideal and worst solution.
[R13] Yoon, K., & Hwang, C. L. (1981). Multiple attribute decision making: methods and applications. SPRINGER-VERLAG BERLIN AN. [R23] TOPSIS. In Wikipedia, The Free Encyclopedia. Retrieved from https://en.wikipedia.org/wiki/TOPSIS [R33] Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
Normalization function for the alternative matrix.
Normalization function for the weights vector.
Create a simply
dictrepresentation of the object.
decide(data[, criteria, weights])
Execute the Solver over the given data.
make_result(data, kernel, rank, extra)
Create a new
Normalize the alternative matrix and weight vector.
Execute the multi-criteria method.