COPPE-Cosenza Fuzzy Hierarchy Model
Aggregate
AggregateMatrix
Aggregation.matrix S4 Class
Aggregation.matrix.default
Aggregation.matrix.membership.difference
as.data.frame
Coppe.cosenza S4 Class
Coppe.cosenza
coppeCosenzaR
Factor S4 Class
Factor Constructor
Factors.of.interest S4 Class
Factors.of.interest Constructor
getFactorsOfInterestNames
getOptionFactorsNames
getOptionPortfolioFactors
getOptionPortfolioNames
getProjectFactorsNames
getProjectFactorsSpecific
getProjectPortfolioFactors
getProjectPortfolioNames
Option S4 Class
Option.factor.availability S4 Class
Option.factor.availability Constructor
Option.portfolio S4 Class
Option.portfolio
Option Constructor function
Option.resources S4 Class
Option.resources Constructor
Project S4 Class
Project.criteria S4 Class
Project.criteria Constructor
Project.criterion S4 Class
Project.criterion
Project.portfolio
Project.portfolio
Project Constructor function
show
summary
The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. The model was based on the evaluation of local alternatives, representing regional potentialities, so as to fulfill demands of economic projects. After defining demand profiles in terms of their technological coefficients, the degree of importance of factors is defined so as to represent the productive activity. The method can detect a surplus of supply without the restriction of the distance of classical algebra, defining a hierarchy of location alternatives. In COPPE-Cosenza Model, the distance between factors is measured in terms of the difference between grades of memberships of the same factors belonging to two or more sets under comparison. The required factors are classified under the following linguistic variables: Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant (I). And the alternatives can assume the following linguistic variables: Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and Inexistent (In). The model also provides flexibility, allowing different aggregation rules to be performed and defined by the Decision Maker. Such feature is considered in this package, allowing the user to define other aggregation matrices, since it considers the same linguistic variables mentioned.
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