Merge pull request #53 from bahadirfyildirim/main

add LOPCOW method

README.md

@@ -126,8 +126,7 @@ Please check out the reference manual [here](https://jbytecode.github.io/JMcDM/)
 - MEREC (MEthod based on the Removal Effects of Criteria) for determining weights
 - PIV (Proximity Indexed Value) method
 - SECA (Simultaneous Evaluation of Criteria and Alternatives)
-
-
+- LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting)
 
 ### SCDM Tools
 
@@ -145,7 +144,6 @@ Please check out the reference manual [here](https://jbytecode.github.io/JMcDM/)
 
 - Game solver for zero sum games
 
-
 ## Unimplemented methods
 - UTA
 - MAUT

src/JMcDM.jl

@@ -30,10 +30,6 @@ function __init__()
     end
 end
 
-
-
-
-
 # Abstract Types 
 abstract type MCDMResult end
 abstract type SCDMResult end
@@ -86,7 +82,6 @@ struct MCDMSetting
     fns::Array{F,1} where {F<:Function}
 end
 
-
 # includes 
 include("greynumber.jl")
 include("utilities.jl")
@@ -117,6 +112,7 @@ include("codas.jl")
 include("psi.jl")
 include("moosra.jl")
 include("merec.jl")
+include("lopcow.jl")
 
 include("summary.jl")
 
@@ -128,9 +124,6 @@ include("piv.jl")
 
 include("copeland.jl")
 
-
-
-
 # imports from modules
 import .Topsis: topsis, TopsisMethod, TopsisResult
 import .WPM: wpm, WPMResult, WPMMethod
@@ -161,7 +154,7 @@ import .Entropy: entropy, EntropyResult
 import .AHP: ahp, ahp_consistency, ahp_RI, AHPResult, AHPConsistencyResult
 import .MEREC: merec, MERECResult, MERECMethod
 import .PIV: piv, PIVResult, PIVMethod
-
+import .LOPCOW: lopcow, LOPCOWResult, LOPCOWMethod
 
 import .SCDM: LaplaceResult, MaximinResult, MaximaxResult, MinimaxResult, MiniminResult
 import .SCDM: SavageResult, HurwiczResult, MLEResult, ExpectedRegretResult
@@ -202,7 +195,7 @@ export MoosraMethod
 export ROVMethod
 export MERECMethod
 export PIVMethod
-
+export LOPCOWMethod
 
 export MCDMSetting
 
@@ -236,8 +229,7 @@ export PSIResult
 export MoosraResult
 export MERECResult
 export PIVResult
-
-
+export LOPCOWResult
 
 #  export SCDM types
 export SCDMResult
@@ -252,8 +244,6 @@ export MLEResult
 export ExpectedRegretResult
 
 
-
-
 # export utility functions
 export euclidean
 export normalize
@@ -292,7 +282,7 @@ export psi
 export moosra
 export merec
 export piv
-
+export lopcow
 
 #  export SCDM tools
 export laplace
@@ -305,8 +295,6 @@ export hurwicz
 export mle
 export expectedregret
 
-
-
 #  export summary function
 export summary
 export mcdm

src/lopcow.jl

@@ -0,0 +1,128 @@
+# LOPCOW
+module LOPCOW
+
+export lopcow, LOPCOWResult, LOPCOWMethod
+
+import ..MCDMMethod, ..MCDMResult, ..MCDMSetting
+import ..Normalizations
+
+using ..Utilities
+
+struct LOPCOWMethod <: MCDMMethod 
+    normalization::G where {G <: Function}
+end
+
+LOPCOWMethod() = LOPCOWMethod(Normalizations.maxminrangenormalization)
+
+struct LOPCOWResult <: MCDMResult
+    decisionMatrix::Matrix
+    w::Vector
+end
+
+"""
+    lopcow(decisionMat, fns; normalization)
+
+Apply LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting) method for a given matrix and criteria types.
+
+# Arguments:
+ - `decisionMat::Matrix`: n × m matrix of objective values for n alternatives and m criteria 
+ - `fns::Array{<:Function, 1}`: m-vector of functions to be applied on the columns.
+ - `normalization{<:Function}`: Optional normalization function.
+
+# Description 
+lopcow() applies the LOPCOW method to calculate objective weights using a decision matrix with  
+n alterntives subject to m criteria which are supposed to be either maximized or minimized.
+
+# Output 
+- `::LOPCOWResult`: LOPCOWResult object that holds multiple outputs including weighting and best index.
+
+# Examples
+```julia-repl
+
+julia> decmat
+9×17 Matrix{Float64}:
+ 21.8  14.1  10.7  1.6  1.8   770.0  12750.0  18.0   5100.0  1.5     9.1    1.054  4.196  29.407   7.03   15.08    9.705
+ 16.4   8.5  13.9  1.2  1.3   524.0  12087.0   5.7   2941.0  2.208  15.2    1.123  3.86    5.228  14.724  32.103  19.0
+ 14.5   7.0   2.3  0.2  0.2   238.0   3265.0   1.9    320.0  2.32   16.202  1.008  3.095   5.549  17.34   65.129  32.056
+ 18.2  10.3  11.4  1.2  1.1   835.0  16037.0  21.3   4332.0  0.875   9.484  0.856  2.191  23.75   13.1    58.157  27.46
+ 18.5   8.1  11.1  1.0  1.1   504.0   9464.0   1.4   1743.0  2.95    0.7    0.479  2.44    8.77   13.48   33.45   17.68
+ 18.7  11.4  10.8  1.3  1.5  1227.0  24053.0  20.0   6521.0  0.733   1.6    0.857  2.377   4.985  11.743  26.732  24.485
+ 18.5  12.6  10.8  1.4  1.8   912.0  18800.0  18.2   5300.0  1.29    8.27   0.558  0.635   5.22   13.829  31.914   7.515
+ 16.4   6.7  12.6  0.9  0.9   951.0  16767.0  22.0   3917.0  2.46    3.9    0.724  0.568   4.491  14.357  28.869   7.313
+ 15.2   6.3   6.9  0.5  0.5  1013.0  20170.0  10.97  4060.0  1.67    1.7    0.704  2.96    3.24   10.029  60.981  23.541
+
+julia> fns = [maximum, maximum, maximum, maximum, maximum, maximum, maximum, maximum, maximum, maximum, minimum, minimum, minimum, minimum, minimum, minimum, minimum ];
+
+julia> result = lopcow(decmat, fns);
+
+julia> result.w
+17-element Vector{Float64}:
+ 0.04947396185854988
+ 0.036623078374935315
+ 0.08456624432002027
+ 0.07055941647198624
+ ⋮
+ 0.07625360895444118
+ 0.05507171491276535
+ 0.05320727577078255
+ 0.05340460620185558
+
+```
+# References
+
+Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
+
+"""
+function lopcow(
+    decisionMat::Matrix, 
+    fns::Array{F,1}; 
+    normalization::G = Normalizations.maxminrangenormalization
+    )::LOPCOWResult where {F<:Function, G<:Function}
+
+    row, col = size(decisionMat)
+    colMax = colmaxs(decisionMat)
+    colMin = colmins(decisionMat)
+
+    A = similar(decisionMat)
+
+    zerotype = eltype(decisionMat[:, 1])
+
+    normalizedMat = normalization(decisionMat, fns)
+
+    PV = zeros(zerotype, col)
+
+    for i = 1:col
+        PV[i] = log(sqrt(sum(normalizedMat[:,i].*normalizedMat[:,i])/row)/std(normalizedMat[:,i]))*100
+    end
+
+    w = zeros(zerotype, col)
+
+    for i = 1:col
+        w[i] = PV[i] ./ sum(PV)
+    end
+
+    result = LOPCOWResult(decisionMat, w)
+
+    return result
+end
+
+"""
+    lopcow(setting)
+
+Apply LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting) method for a given matrix and criteria types.
+
+# Arguments:
+ - `setting::MCDMSetting`: MCDMSetting object. 
+ 
+# Description 
+lopcow() applies the LOPCOW method to rank n alterntives subject to m criteria which are supposed to be 
+either maximized or minimized.
+
+# Output 
+- `::LOPCOWResult`: LOPCOWResult object that holds multiple outputs including weighting and best index.
+"""
+function lopcow(setting::MCDMSetting)::LOPCOWResult
+    lopcow(setting.df, setting.fns)
+end
+
+end # end of module LOPCOW

src/mcdm.jl

@@ -29,6 +29,7 @@ julia> subtypes(MCDMMethod)
  EdasMethod
  ElectreMethod
  GreyMethod
+ LOPCOWMethod
  MERECMethod
  MabacMethod
  MaircaMethod
@@ -107,6 +108,8 @@ function mcdm(
         psi(df, fns)
     elseif method isa MERECMethod
         merec(df, fns)
+    elseif method isa LOPCOWMethod
+        merec(df, fns)
     elseif method isa PIVMethod
         piv(df, w, fns)
     else

test/runtests.jl

@@ -1,13 +1,11 @@
 using Test
 
-
 using JuMP, Ipopt, JMcDM
 
 import JMcDM.Game: game
 import JMcDM.DataEnvelop: dataenvelop
 import JMcDM.SECA: seca
 
-
 const testGreyNumbers = true
 const testUtilityFunctions = true
 const testMCDMFunctions = true
@@ -16,19 +14,16 @@ const testLPBasedFunctions = true
 const testGreyMCDMFunctions = true
 const testSummary = true
 
-
 if testGreyNumbers
     @info "Grey Numbers tests ..."
     include("./testgreynumber.jl")
 end
 
-
 if testUtilityFunctions
     @info "Utility tests ..."
     include("./testutility.jl")
 end
 
-
 if testSCDMFunctions
     @info "SCDM tests ..."
     include("./testscdm.jl")