forked from jbytecode/JMcDM
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
bahadirfyildirim
committed
Nov 17, 2023
1 parent
12c1c40
commit 1619adb
Showing
5 changed files
with
236 additions
and
31 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| module OCRA | ||
|
|
||
| export ocra, OCRAResult, OCRAMethod | ||
|
|
||
| import ..MCDMMethod, ..MCDMResult, ..MCDMSetting | ||
|
|
||
| using ..Utilities | ||
|
|
||
| struct OCRAResult <: MCDMResult | ||
| decisionMatrix::Matrix | ||
| weights::Array{Float64,1} | ||
| scores::Vector | ||
| ranking::Array{Int64,1} | ||
| bestIndex::Int64 | ||
| end | ||
|
|
||
| """ | ||
| 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} | ||
|
|
||
| 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) | ||
|
|
||
| colMax = colmaxs(decisionMat) | ||
| colMin = colmins(decisionMat) | ||
|
|
||
| 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 | ||
|
|
||
| I2 = I1 .- minimum(I1) | ||
| O2 = O1 .- minimum(O1) | ||
| scores = I2 .+ O2 | ||
| scores = scores .- minimum(scores) | ||
|
|
||
| rankings = sortperm(scores) | ||
|
|
||
| bestIndex = rankings |> last | ||
|
|
||
| result = OCRAResult(decisionMat, w, scores, rankings, bestIndex) | ||
|
|
||
| return result | ||
| end | ||
|
|
||
| """ | ||
| 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 | ||
|
|
||
| end # end of module OCRA |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters