# 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 decision-making problems with multiple objectives. The method solves successive scenarios formulated as linear programs. For each scenario, the decision-maker 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. Decision : skcriteria.madm.Decision With values: 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. 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 or e_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 n-dimensional array. When th n-th row represent the result values of the variables for the n-th 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.

References

  Munier, N. (2011). A strategy for using multicriteria analysis in decision-making: a guide for simple and complex environmental projects. Springer Science & Business Media.
  Munier, N., Carignano, C., & Alberto, C. UN MÉTODO DE PROGRAMACIÓN MULTIOBJETIVO. Revista de la Escuela de Perfeccionamiento en Investigación Operativa, 24(39).
Attributes: mnorm Normalization function for the alternative matrix. njobs How many cores to use to solve the linear programs and the second method. solver Which solver to use to solve the undelying linear programs. wnorm Normalization function for the weights vector.

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 multi-criteria 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 multi-criteria method.

Parameters: data : skcriteria.Data Preprocessed Data. object object or tuple of objects with the raw result data.
solver

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