skcriteria.madm.simus
module¶
SIMUS (Sequential Interactive Model for Urban Systems) Method

class
skcriteria.madm.simus.
SIMUS
(mnorm='none', wnorm='none', rank_by=1, solver='pulp', njobs=None)[source]¶ Bases:
skcriteria.madm._dmaker.DecisionMaker
SIMUS (Sequential Interactive Model for Urban Systems) developed by Nolberto Munier (2011) is a tool to aid decisionmaking problems with multiple objectives. The method solves successive scenarios formulated as linear programs. For each scenario, the decisionmaker must choose the criterion to be considered objective while the remaining restrictions constitute the constrains system that the projects are subject to. In each case, if there is a feasible solution that is optimum, it is recorded in a matrix of efficient results. Then, from this matrix two rankings allow the decision maker to compare results obtained by different procedures. The first ranking is obtained through a linear weighting of each column by a factor  equivalent of establishing a weight  and that measures the participation of the corresponding project. In the second ranking, the method uses dominance and subordinate relationships between projects, concepts from the French school of MCDM.
Parameters:  mnorm : string, callable, optional (default=”none”)
Normalization method for the alternative matrix.
 wnorm : string, callable, optional (default=”none”)
Normalization method for the weights array.
 rank_by : 1 or 2 (default=1)
Wich of the two methods are used to calculate the ranking. The two methods are executed always.
 solver : str, default=”pulp”
Which solver to use to solve the undelying linear programs. The full list are available in skcriteria.utils.lp.SOLVERS
 njobs : int, default=None
How many cores to use to solve the linear programs and the second method. By default all the availables cores are used.
Returns:  Decision :
skcriteria.madm.Decision
With values:
 kernel_: None
 rank_: A ranking (start at 1) where the inth element represent the position of the inth 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.
 rank_by: 1 or 2. Wich of the two methods are used to
calculate the ranking. Esentialy if the rank is calculated with
e_.points1
ore_points2
 solver: With solver was used for the underlying linear problems.
 stages: The underlying linear problems.
 stage_results: The values of the variables of the linear problems as a ndimensional array. When th nth row represent the result values of the variables for the nth stage.
 points1: The points of every alternative obtained by the first method.
 points2: The points of every alternative obtained by the first method.
 tita_j_p: 2nd. method domination.
 tita_j_d: 2nd. method subordination.
 doms: Total dominance matrix of the 2nd. method.
 dom_by_crit: Dominance by criteria of the 2nd method.
 rank_by: 1 or 2. Wich of the two methods are used to
calculate the ranking. Esentialy if the rank is calculated with
References
[1] Munier, N. (2011). A strategy for using multicriteria analysis in decisionmaking: a guide for simple and complex environmental projects. Springer Science & Business Media. [2] Munier, N., Carignano, C., & Alberto, C. UN MÉTODO DE PROGRAMACIÓN MULTIOBJETIVO. Revista de la Escuela de Perfeccionamiento en Investigación Operativa, 24(39). Attributes: Methods
as_dict
()Create a simply dict
representation of the object.decide
(data[, criteria, weights])Execute the Solver over the given data. doc_inherit
make_result
(data, kernel, rank, extra)Create a new skcriteria.madm.Decision
preprocess
(data)Normalize the alternative matrix and weight vector. solve
(ndata, b)Execute the multicriteria method. 
doc_inherit
= functools.partial(<function _doc_inherit>, (<class 'skcriteria.madm._dmaker.DecisionMaker'>, <class 'skcriteria.base.BaseSolver'>, <class 'object'>))¶

njobs
¶ How many cores to use to solve the linear programs and the second method. By default all the availables cores are used.

solve
(ndata, b)[source]¶ Execute the multicriteria method.
Parameters:  data :
skcriteria.Data
Preprocessed Data.
Returns: object
object or tuple of objects with the raw result data.
 data :

solver
¶ Which solver to use to solve the undelying linear programs. The full list are available in skcriteria.utils.lp.SOLVERS