modelfuzzy_possibilistic {deaR}

Possibilistic Fuzzy DEA model.

Description

Solve the possibilistic fuzzy DEA model proposed by León et. al (2003).

Usage

modelfuzzy_possibilistic(datadea,
                         dmu_eval = NULL,
                         poss_modelname = c("basic"),
                         h = 1,
                         ...)

Arguments

datadea

A deadata_fuzzy object, including DMUs, inputs and outputs.

dmu_eval

A numeric vector containing which DMUs have to be evaluated. If NULL (default), all DMUs are considered.

poss_modelname

a string containing the name of the model.

h

A numeric vector with the h-levels (in [0,1]).

...

dmu_ref, orientation, rts and other model parameters.

Value

An object of class deadata_fuzzy.

Author(s)

Vicente Coll-Serrano ([e-mail]). Quantitative Methods for Measuring Culture (MC2). Applied Economics.

Vicente Bolós ([e-mail]). Department of Business Mathematics

Rafael Benítez ([e-mail]). Department of Business Mathematics

University of Valencia (Spain)

References

Emrouznejad, A.; Tavana, M.; Hatami-Marbini, A. (2014). “The State of the Art in Fuzzy Data Envelopment Analysis”, in A. Emrouznejad and M. Tavana (eds.), Performance Measurement with Fuzzy Data Envelopment Analysis. Studies in Fuzziness and Soft Computing 309. Springer, Berlin. doi:10.1007/978-3-642-41372-8_1

Hatami-Marbini, A.; Emrouznejad, A.; Tavana, M. (2011). "A Taxonomy and Review of the Fuzzy Data Envelopment Analysis Literature: Two Decades in the Making", European Journal of Operational Research, 214, 457–472. doi:10.1016/j.ejor.2011.02.001

Leon, T.; Liern, V. Ruiz, J.; Sirvent, I. (2003). "A Possibilistic Programming Approach to the Assessment of Efficiency with DEA Models", Fuzzy Sets and Systems, 139, 407–419. doi:10.1016/S0165-0114(02)00608-5

See Also

model_basic, modelfuzzy_kaoliu, modelfuzzy_guotanaka

Examples

# Replication of results in Leon et. al (2003, p. 416)
data("Leon2003")
data_example <- make_deadata_fuzzy(Leon2003,
                                   inputs.mL = 2, 
                                   inputs.dL = 3, 
                                   outputs.mL = 4, 
                                   outputs.dL = 5)
result <- modelfuzzy_possibilistic(data_example, 
                                   h = seq(0, 1, by = 0.1), 
                                   orientation = "io", 
                                   rts = "vrs")
efficiencies(result)