https://github.com/javierbarbero/DataEnvelopmentAnalysis.jl
Tip revision: 13e58d00a16fc2bfb370d0b2d53af3187404d330 authored by Javier Barbero on 25 February 2020, 22:20:48 UTC
Compatibility with JuMP 0.21
Compatibility with JuMP 0.21
Tip revision: 13e58d0
deaadd.jl
# This file contains functions for the Additive DEA model
"""
AdditivelDEAModel
An data structure representing an additive DEA model.
"""
struct AdditiveDEAModel <: AbstractTechnicalDEAModel
n::Int64
m::Int64
s::Int64
rts::Symbol
eff::Vector
slackX::Matrix
slackY::Matrix
lambda::SparseMatrixCSC{Float64, Int64}
weights::Symbol
end
"""
deaadd(X, Y, model)
Compute related data envelopment analysis weighted additive models for inputs `X` and outputs `Y`.
Model specification:
- `:Ones`: standard additive DEA model.
- `:MIP`: Measure of Inefficiency Proportions. (Charnes et al., 1987; Cooper et al., 1999)
- `:Normalized`: Normalized weighted additive DEA model. (Lovell and Pastor, 1995)
- `:RAM`: Range Adjusted Measure. (Cooper et al., 1999)
- `:BAM`: Bounded Adjusted Measure. (Cooper et al, 2011)
- `:Custom`: User supplied weights.
# Optional Arguments
- `rts=:VRS`: chosse between constant returns to scale `:CRS` or variable returns to scale `:VRS`.
- `wX`: matrix of weights of inputs. Only if `model=:Custom`.
- `WY`: matrix of weights of outputs. Only if `model=:Custom`.
- `Xref=X`: Identifies the reference set of inputs against which the units are evaluated.
- `Yref=Y`: Identifies the reference set of outputs against which the units are evaluated.
# Examples
```jldoctest
julia> X = [5 13; 16 12; 16 26; 17 15; 18 14; 23 6; 25 10; 27 22; 37 14; 42 25; 5 17];
julia> Y = [12; 14; 25; 26; 8; 9; 27; 30; 31; 26; 12];
julia> deaadd(X, Y, :MIP)
Weighted Additive DEA Model
DMUs = 11; Inputs = 2; Outputs = 1
Weights = MIP; Returns to Scale = VRS
─────────────────────────────────────────────────────
efficiency slackX1 slackX2 slackY1
─────────────────────────────────────────────────────
1 0.0 0.0 0.0 0.0
2 0.507519 0.0 0.0 7.10526
3 0.0 0.0 0.0 0.0
4 -4.72586e-17 -8.03397e-16 0.0 0.0
5 2.20395 0.0 0.0 17.6316
6 1.31279e-16 8.10382e-16 0.0 8.64407e-16
7 0.0 0.0 0.0 0.0
8 0.0 0.0 0.0 0.0
9 0.0 0.0 0.0 0.0
10 1.04322 17.0 15.0 1.0
11 0.235294 0.0 4.0 0.0
─────────────────────────────────────────────────────
```
"""
function deaadd(X::Matrix, Y::Matrix, model::Symbol = :Default; rts::Symbol = :VRS,
wX::Matrix = Array{Float64}(undef, 0, 0), wY::Matrix = Array{Float64}(undef, 0, 0),
Xref::Matrix = X, Yref::Matrix = Y)::AdditiveDEAModel
# Check parameters
nx, m = size(X)
ny, s = size(Y)
nrefx, mref = size(Xref)
nrefy, sref = size(Yref)
if nx != ny
error("number of observations is different in inputs and outputs")
end
if nrefx != nrefy
error("number of observations is different in inputs reference set and ouputs reference set")
end
if m != mref
error("number of inputs in evaluation set and reference set is different")
end
if s != sref
error("number of outputs in evaluation set and reference set is different")
end
# Default behaviour
if model == :Default
# If no weights are specified use :Ones
if length(wX) == 0 && length(wY) == 0
model = :Ones
else
model = :Custom
end
end
# Get weights based on the selected model
if model != :Custom
# Display error if both model and weights are specified
if length(wX) != 0 || length(wY) != 0
error("Weights not allowed if model != :Custom")
end
# Get weights for selected model
wX, wY = deaaddweights(X, Y, model)
end
if size(wX) != size(X)
error("size of weights matrix for inputs should be equal to size of inputs")
end
if size(wY) != size(Y)
error("size of weights matrix for outputs should be qual to size of outputs")
end
# Parameters for additional condition in BAM model
minXref = minimum(X, dims = 1)
maxYref = maximum(Y, dims = 1)
# Compute efficiency for each DMU
n = nx
nref = nrefx
effi = zeros(n)
slackX = zeros(n, m)
slackY = zeros(n, s)
lambdaeff = spzeros(n, nref)
for i=1:n
# Value of inputs and outputs to evaluate
x0 = X[i,:]
y0 = Y[i,:]
# Value of weights to evaluate
wX0 = wX[i,:]
wY0 = wY[i,:]
# Create the optimization model
deamodel = Model(GLPK.Optimizer)
@variable(deamodel, sX[1:m] >= 0)
@variable(deamodel, sY[1:s] >= 0)
@variable(deamodel, lambda[1:nref] >= 0)
@objective(deamodel, Max, sum(wX0[j] * sX[j] for j in 1:m) + sum(wY0[j] * sY[j] for j in 1:s) )
@constraint(deamodel, [j in 1:m], sum(Xref[t,j] * lambda[t] for t in 1:nref) == x0[j] - sX[j])
@constraint(deamodel, [j in 1:s], sum(Yref[t,j] * lambda[t] for t in 1:nref) == y0[j] + sY[j])
# Add return to scale constraints
if rts == :CRS
# Add constraints for BAM CRS model
if model == :BAM
@constraint(deamodel, [j in 1:m], sum(Xref[t,j] * lambda[t] for t in 1:nref) >= minXref[j])
@constraint(deamodel, [j in 1:s], sum(Yref[t,j] * lambda[t] for t in 1:nref) <= maxYref[j])
end
elseif rts == :VRS
@constraint(deamodel, sum(lambda) == 1)
else
error("Invalid returns to scale $rts. Returns to scale should be :CRS or :VRS")
end
# Fix values of slacks when weight are zero
for j = 1:m
if wX0[j] == 0
fix(sX[j], 0, force = true)
end
end
for j = 1:s
if wY0[j] == 0
fix(sY[j], 0, force = true)
end
end
# Optimize and return results
JuMP.optimize!(deamodel)
effi[i] = JuMP.objective_value(deamodel)
lambdaeff[i,:] = JuMP.value.(lambda)
slackX[i,:] = JuMP.value.(sX)
slackY[i,:] = JuMP.value.(sY)
# Check termination status
if termination_status(deamodel) != MOI.OPTIMAL
@warn ("DMU $i termination status: $(termination_status(deamodel)). Primal status: $(primal_status(deamodel)). Dual status: $(dual_status(deamodel))")
end
end
return AdditiveDEAModel(n, m, s, rts, effi, slackX, slackY, lambdaeff, model)
end
function deaadd(X::Vector, Y::Matrix, model::Symbol = :Default; rts::Symbol = :VRS,
wX::Vector = Array{Float64}(undef, 0), wY::Matrix = Array{Float64}(undef, 0, 0),
Xref::Vector = X, Yref::Matrix = Y)::AdditiveDEAModel
X = X[:,:]
wX = wX[:,:]
Xref = Xref[:,:]
return deaadd(X, Y, model, rts = rts, wX = wX, wY = wY, Xref = Xref, Yref = Yref)
end
function deaadd(X::Matrix, Y::Vector, model::Symbol = :Default; rts::Symbol = :VRS,
wX::Matrix = Array{Float64}(undef, 0, 0), wY::Vector = Array{Float64}(undef, 0),
Xref::Matrix = X, Yref::Vector = Y)::AdditiveDEAModel
Y = Y[:,:]
wY = wY[:,:]
Yref = Yref[:,:]
return deaadd(X, Y, model, rts = rts, wX = wX, wY = wY, Xref = Xref, Yref = Yref)
end
function deaadd(X::Vector, Y::Vector, model::Symbol = :Default; rts::Symbol = :VRS,
wX::Vector = Array{Float64}(undef, 0), wY::Vector = Array{Float64}(undef, 0),
Xref::Vector = X, Yref::Vector = Y)::AdditiveDEAModel
X = X[:,:]
wX = wX[:,:]
Xref = Xref[:,:]
Y = Y[:,:]
wY = wY[:,:]
Yref = Yref[:,:]
return deaadd(X, Y, model, rts = rts, wX = wX, wY = wY, Xref = Xref, Yref = Yref)
end
function Base.show(io::IO, x::AdditiveDEAModel)
compact = get(io, :compact, false)
n = nobs(x)
m = ninputs(x)
s = noutputs(x)
eff = efficiency(x)
slackX = slacks(x, :X)
slackY = slacks(x, :Y)
if !compact
print(io, "Weighted Additive DEA Model \n")
print(io, "DMUs = ", n)
print(io, "; Inputs = ", m)
print(io, "; Outputs = ", s)
print(io, "\n")
print(io, "Weights = ", string(x.weights))
print(io, "; Returns to Scale = ", string(x.rts))
print(io, "\n")
show(io, CoefTable(hcat(eff, slackX, slackY), ["efficiency"; ["slackX$i" for i in 1:m ]; ; ["slackY$i" for i in 1:s ]], ["$i" for i in 1:n]))
end
end
"""
deaaddweights(X, Y, model)
Compute corresponding weights for related data envelopment analysis weighted additive models for inputs `X` and outputs `Y`.
Model specification:
- `:Ones`: standard additive DEA model.
- `:MIP`: Measure of Inefficiency Proportions. (Charnes et al., 1987; Cooper et al., 1999)
- `:Normalized`: Normalized weighted additive DEA model. (Lovell and Pastor, 1995)
- `:RAM`: Range Adjusted Measure. (Cooper et al., 1999)
- `:BAM`: Bounded Adjusted Measure. (Cooper et al, 2011)
"""
function deaaddweights(X::Matrix, Y::Matrix, model::Symbol)
# Compute specific weights based on the model
if model == :Ones
# Standard Additive DEA model
wX = ones(size(X))
wY = ones(size(Y))
elseif model == :MIP
# Measure of Inefficiency Proportions
wX = 1 ./ X
wY = 1 ./ Y
elseif model == :Normalized
# Normalized weighted additive DEA model
wX = zeros(size(X))
wY = zeros(size(Y))
m = size(X, 2)
s = size(Y, 2)
for i=1:m
wX[:,i] .= 1 ./ std(X[:,i])
end
for i=1:s
wY[:,i] .= 1 ./ std(Y[:,i])
end
wX[isinf.(wX)] .= 0
wY[isinf.(wY)] .= 0
elseif model == :RAM
# Range Adjusted Measure
m = size(X, 2)
s = size(Y, 2)
wX = zeros(size(X))
wY = zeros(size(Y))
for i=1:m
wX[:,i] .= 1 ./ ((m + s) * (maximum(X[:,i]) - minimum(X[:,i])))
end
for i=1:s
wY[:,i] .= 1 ./ ((m + s) * (maximum(Y[:,i]) - minimum(Y[:,i])))
end
wX[isinf.(wX)] .= 0
wY[isinf.(wY)] .= 0
elseif model == :BAM
# Bounded Adjusted Measure
m = size(X, 2)
s = size(Y, 2)
minX = zeros(m)
maxY = zeros(s)
wX = zeros(size(X))
wY = zeros(size(Y))
for i=1:m
minX[i] = minimum(X[:,i])
wX[:,i] = 1 ./ ((m + s) .* (X[:,i] .- minX[i] ))
end
for i=1:s
maxY[i] = maximum(Y[:,i])
wY[:,i] = 1 ./ ((m + s) .* (maxY[i] .- Y[:,i]))
end
wX[isinf.(wX)] .= 0
wY[isinf.(wY)] .= 0
else
error("Invalid model ", model)
end
return wX, wY
end