Computes ranks for the given DMUs (alternatives) using SMAA-D method and Additive Data Envelopment Analysis Model with imprecise data. For given number of samples returns a matrix with alternatives in each row and rankings in each column. Single cell indicates how many samples of respective alternative gave respective position in ranking. ]]> A list of alternatives (DMUs). [...] [...] ]]> A list of criteria with specified preference direction. List has to contains at least one criterion that will be minimized (input) and at least one criterion that will be maximized (output). [...] [...] [...] ]]> A list of alternatives (DMUs) minimal performances (or exact performances if intervals should be created by tolerance). [...] [...] [...] [...] [...] ]]> A list of alternatives (DMUs) maximal performances. [...] [...] [...] [...] [...] ]]> A list of criteria linear constraints. [...] [...] [...] [...] [...] [...] ]]> Represents method parameters. "tolerance" represents the fraction for creating interval data (created interval: [data*(1-tolerance), data*(1+tolerance)]), "transformToUtilities" means if data should be tranformed into values from range [0-1], "boundariesProvided" means if inputsOutputs file contains information about min and max data for each factor, "functionShapeProvided" means if inputsOutputs file contains information about the shapes of value function for given factor, "samplesNb" determines number of samples used to calculate results. %1 %2 %3 %4 %5 ]]> The value should be a positive integer. 0]]> 100 The value should be non-negative. = 0]]> 0.0 true false false

A performance table for given alternatives. Single performance consists of attribute criterionID representing certain ranking, and a value representing ratio of samples attaining this ranking. [...] Rank [...] [...] [...] ]]>

A list of alternatives with average rank (obtained for given sample). [...] [...] [...] ]]>

A list of messages generated by the algorithm.