coppeCosenzaR0.1.3 package

COPPE-Cosenza Fuzzy Hierarchy Model

Aggregate

Aggregate

AggregateMatrix

AggregateMatrix

Aggregation.matrix-class

Aggregation.matrix S4 Class

Aggregation.matrix.default-class

Aggregation.matrix.default

Aggregation.matrix.membership.difference-class

Aggregation.matrix.membership.difference

as.data.frame

as.data.frame

Coppe.cosenza-class

Coppe.cosenza S4 Class

Coppe.cosenza

Coppe.cosenza

coppeCosenzaR

coppeCosenzaR

Factor-class

Factor S4 Class

Factor

Factor Constructor

Factors.of.interest-class

Factors.of.interest S4 Class

Factors.of.interest

Factors.of.interest Constructor

getFactorsOfInterestNames

getFactorsOfInterestNames

getOptionFactorsNames

getOptionFactorsNames

getOptionPortfolioFactors

getOptionPortfolioFactors

getOptionPortfolioNames

getOptionPortfolioNames

getProjectFactorsNames

getProjectFactorsNames

getProjectFactorsSpecific

getProjectFactorsSpecific

getProjectPortfolioFactors

getProjectPortfolioFactors

getProjectPortfolioNames

getProjectPortfolioNames

Option-class

Option S4 Class

Option.factor.availability-class

Option.factor.availability S4 Class

Option.factor.availability

Option.factor.availability Constructor

Option.portfolio-class

Option.portfolio S4 Class

Option.portfolio

Option.portfolio

Option

Option Constructor function

Option.resources-class

Option.resources S4 Class

Option.resources

Option.resources Constructor

Project-class

Project S4 Class

Project.criteria-class

Project.criteria S4 Class

Project.criteria

Project.criteria Constructor

Project.criterion-class

Project.criterion S4 Class

Project.criterion

Project.criterion

Project.portfolio-class

Project.portfolio

Project.portfolio

Project.portfolio

Project

Project Constructor function

show

show

summary

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.

  • Maintainer: Pier Taranti
  • License: GPL-2 | file LICENSE
  • Last published: 2017-10-28