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add OCRA method
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| Original file line number | Diff line number | Diff line change |
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| module OCRA | ||
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| export ocra, OCRAResult, OCRAMethod | ||
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| import ..MCDMMethod, ..MCDMResult, ..MCDMSetting | ||
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| using ..Utilities | ||
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| struct OCRAResult <: MCDMResult | ||
| decisionMatrix::Matrix | ||
| weights::Array{Float64,1} | ||
| scores::Vector | ||
| ranking::Array{Int64,1} | ||
| bestIndex::Int64 | ||
| end | ||
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| """ | ||
| ocra(decisionMat, weights, fns) | ||
| Apply ORCA (Operational Competitiveness RAting) for a given matrix and weights. | ||
| # Arguments: | ||
| - `decisionMat::Matrix`: m × n matrix of objective values for n alternatives and m criteria | ||
| - `weights::Array{Float64, 1}`: n-vector of weights that sum up to 1.0. If the sum of weights is not 1.0, it is automatically normalized. | ||
| - `fns::Array{<:Function, 1}`: n-vector of functions to be applied on the columns. | ||
| # Description | ||
| ocra() applies the OCRA method to rank m alternatives subject to n criteria which are supposed to be | ||
| either maximized or minimized. | ||
| # Output | ||
| - `::OCRAResult`: OCRAResult object that holds multiple outputs including scores, rankings, and best index. | ||
| # Examples | ||
| ```julia-repl | ||
| julia> decMat = [ | ||
| 8.0 16.0 1.5 1.2 4200.0 5.0 5.0 314.0 185.0; | ||
| 8.0 16.0 1.0 1.3 4200.0 5.0 4.0 360.0 156.0; | ||
| 10.1 16.0 2.0 1.3 4060.0 5.0 3.0 503.0 160.0; | ||
| 10.1 8.0 1.0 1.5 5070.0 2.0 4.0 525.0 200.0; | ||
| 10.0 16.0 2.0 1.2 6350.0 5.0 3.0 560.0 190.0; | ||
| 10.1 16.0 1.0 1.2 5500.0 2.0 2.0 521.0 159.0; | ||
| 10.1 64.0 2.0 1.7 5240.0 5.0 3.0 770.0 199.0; | ||
| 7.0 32.0 1.0 1.8 3000.0 3.0 4.0 364.0 157.0; | ||
| 10.1 16.0 1.0 1.3 3540.0 5.0 3.0 510.0 171.0; | ||
| 9.7 16.0 2.0 1.83 7500.0 6.0 2.0 550.0 170.0 | ||
| ] | ||
| 10×9 Matrix{Float64}: | ||
| 8.0 16.0 1.5 1.2 4200.0 5.0 5.0 314.0 185.0 | ||
| 8.0 16.0 1.0 1.3 4200.0 5.0 4.0 360.0 156.0 | ||
| 10.1 16.0 2.0 1.3 4060.0 5.0 3.0 503.0 160.0 | ||
| 10.1 8.0 1.0 1.5 5070.0 2.0 4.0 525.0 200.0 | ||
| 10.0 16.0 2.0 1.2 6350.0 5.0 3.0 560.0 190.0 | ||
| 10.1 16.0 1.0 1.2 5500.0 2.0 2.0 521.0 159.0 | ||
| 10.1 64.0 2.0 1.7 5240.0 5.0 3.0 770.0 199.0 | ||
| 7.0 32.0 1.0 1.8 3000.0 3.0 4.0 364.0 157.0 | ||
| 10.1 16.0 1.0 1.3 3540.0 5.0 3.0 510.0 171.0 | ||
| 9.7 16.0 2.0 1.83 7500.0 6.0 2.0 550.0 170.0 | ||
| julia> weights =[0.167, 0.039, 0.247, 0.247, 0.116, 0.02, 0.056, 0.027, 0.081]; | ||
| julia> fns = [maximum,maximum,maximum,maximum,maximum,maximum,maximum,minimum,minimum]; | ||
| julia> result = ocra(decmat, weights, fns); | ||
| julia> result.scores | ||
| 10-element Vector{Float64}: | ||
| 0.14392093908214929 | ||
| 0.024106550710436096 | ||
| 0.27342011595623067 | ||
| 0.04297916544177691 | ||
| 0.31851953804157623 | ||
| 0.0024882426914910674 | ||
| 0.5921715172301161 | ||
| 0.11390289470614312 | ||
| 0.0 | ||
| 0.47874854984718046 | ||
| julia> result.bestIndex | ||
| 7 | ||
| ``` | ||
| # References | ||
| - Parkan, C. (1994). Operational competitiveness ratings of production units. Managerial and Decision Economics, 15(3), 201–221. doi:10.1002/mde.4090150303 | ||
| - Parkan, C. (2003). Measuring the effect of a new point of sale system on the performance of drugstore operations. Computers & Operations Research, 30(5), 729–744. doi:10.1016/s0305-0548(02)00047-3 | ||
| - Kundakcı, N. (2017). An Integrated Multi-Criteria Decision Making Approach for Tablet Computer Selection. European Journal of Multidisciplinary Studies, 2(5), 31-43. | ||
| """ | ||
| function ocra( | ||
| decisionMat::Matrix, | ||
| weights::Array{Float64,1}, | ||
| fns::Array{F,1}; | ||
| )::OCRAResult where {F<:Function} | ||
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| row, col = size(decisionMat) | ||
| w = unitize(weights) | ||
| zerotype = eltype(decisionMat) | ||
| I1 = zeros(zerotype, row) | ||
| I2 = zeros(zerotype, row) | ||
| O1 = zeros(zerotype, row) | ||
| O2 = zeros(zerotype, row) | ||
| scores = zeros(zerotype, row) | ||
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| colMax = colmaxs(decisionMat) | ||
| colMin = colmins(decisionMat) | ||
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| for i = 1:row | ||
| for j = 1:col | ||
| if fns[j] == minimum | ||
| I1[i] += w[j]*(colMax[j] - decisionMat[i,j])/colMin[j] | ||
| else | ||
| O1[i] += w[j]*(decisionMat[i,j] - colMin[j])/colMin[j] | ||
| end | ||
| end | ||
| end | ||
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| I2 = I1 .- minimum(I1) | ||
| O2 = O1 .- minimum(O1) | ||
| scores = I2 .+ O2 | ||
| scores = scores .- minimum(scores) | ||
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| rankings = sortperm(scores) | ||
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| bestIndex = rankings |> last | ||
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| result = OCRAResult(decisionMat, w, scores, rankings, bestIndex) | ||
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| return result | ||
| end | ||
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| """ | ||
| ocra(setting) | ||
| Apply OCRA (Operational Competitiveness RAting) for a given matrix and weights. | ||
| # Arguments: | ||
| - `setting::MCDMSetting`: MCDMSetting object. | ||
| # Description | ||
| ocra() applies the OCRA method to rank m alternatives subject to n criteria which are supposed to be | ||
| either maximized or minimized. | ||
| # Output | ||
| - `::OCRAResult`: OCRAResult object that holds multiple outputs including scores, rankings, and best index. | ||
| """ | ||
| function ocra(setting::MCDMSetting)::OCRAResult | ||
| ocra(setting.df, setting.weights, setting.fns) | ||
| end | ||
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| end # end of module OCRA |
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