add LMAW method

README.md

@@ -107,6 +107,7 @@ Please check out the reference manual [here](https://jbytecode.github.io/JMcDM/)
 - ELECTRE (Elimination and Choice Translating Reality)
 - Entropy
 - GRA (Grey Relational Analysis)
+- LMAW (Logarithm Methodology of Additive Weights)
 - LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting)
 - MABAC (Multi-Attributive Border Approximation area Comparison)
 - MAIRCA (Multi Attributive Ideal-Real Comparative Analysis)

src/JMcDM.jl

@@ -114,6 +114,7 @@ include("moosra.jl")
 include("merec.jl")
 include("lopcow.jl")
 include("ocra.jl")
+include("lmaw.jl")
 
 include("summary.jl")
 
@@ -157,6 +158,7 @@ import .MEREC: merec, MERECResult, MERECMethod
 import .PIV: piv, PIVResult, PIVMethod
 import .LOPCOW: lopcow, LOPCOWResult, LOPCOWMethod
 import .OCRA: ocra, OCRAResult, OCRAMethod
+import .LMAW: lmaw, LMAWResult, LMAWMethod
 
 import .SCDM: LaplaceResult, MaximinResult, MaximaxResult, MinimaxResult, MiniminResult
 import .SCDM: SavageResult, HurwiczResult, MLEResult, ExpectedRegretResult
@@ -199,6 +201,7 @@ export MERECMethod
 export PIVMethod
 export LOPCOWMethod
 export OCRAMethod
+export LMAWMethod
 
 
 export MCDMSetting
@@ -235,6 +238,7 @@ export MERECResult
 export PIVResult
 export LOPCOWResult
 export OCRAResult
+export LMAWResult
 
 #  export SCDM types
 export SCDMResult
@@ -289,6 +293,7 @@ export merec
 export piv
 export lopcow
 export ocra
+export lmaw
 
 #  export SCDM tools
 export laplace

src/lmaw.jl

@@ -0,0 +1,140 @@
+
+module LMAW
+
+export lmaw, LMAWResult, LMAWMethod
+
+import ..MCDMMethod, ..MCDMResult, ..MCDMSetting
+import ..Normalizations 
+
+using ..Utilities
+
+struct LMAWResult <: MCDMResult
+    decisionMatrix::Matrix
+    weights::Array{Float64,1}
+    scores::Vector
+    ranking::Array{Int64,1}
+    bestIndex::Int64
+end
+
+struct LMAWMethod <: MCDMMethod 
+    normalization::G where {G <: Function}
+end
+
+LMAWMethod() = LMAWMethod(Normalizations.dividebycolumnmaxminnormalization)
+
+"""
+    lmaw(decisionMat, weights, fns; normalization)
+
+Apply LMAW (Logarithm Methodology of Additive Weights) 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.
+ - `normalization{<:Function}`: Optional normalization function.
+
+# Description 
+lmaw() applies the LMAW method to rank m alternatives subject to n criteria which are supposed to be 
+either maximized or minimized.
+
+# Output 
+- `::LMAWResult`: LMAWResult object that holds multiple outputs including scores, rankings, and best index.
+
+# Examples
+```julia-repl
+julia> decMat = [
+                647.34        6.24        49.87        19.46        212.58        6.75;
+                115.64        3.24        16.26         9.69        207.59        3.00;
+                373.61        5.00        26.43        12.00        184.62        3.74;
+                 37.63        2.48         2.85         9.25        142.50        3.24;
+                858.01        4.74        62.85        45.96        267.95        4.00;
+                222.92        3.00        19.24        21.46        221.38        3.49
+                ]   
+       6×6 Matrix{Float64}:
+       647.34  6.24  49.87  19.46  212.58  6.75
+       115.64  3.24  16.26   9.69  207.59  3.0
+       373.61  5.0   26.43  12.0   184.62  3.74
+        37.63  2.48   2.85   9.25  142.5   3.24
+       858.01  4.74  62.85  45.96  267.95  4.0
+       222.92  3.0   19.24  21.46  221.38  3.49
+
+julia> weights = [0.215, 0.126, 0.152, 0.091, 0.19, 0.226];
+
+julia> fns = [maximum, maximum, minimum, minimum, minimum, maximum];
+
+julia> result = lmaw(decmat, weights, fns);
+
+julia> result.scores
+6-element Vector{Float64}:
+ 4.839005264308832
+ 4.679718180594332
+ 4.797731427991642
+ 4.732145373983716
+ 4.73416833375772
+ 4.702247270959649
+
+julia> result.bestIndex
+1
+```
+
+# References
+
+- Pamučar, D., Žižović, M., Biswas, S., & Božanić, D. (2021). A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics. Facta Universitatis, Series: Mechanical Engineering, 19(3), 361. https://doi.org/10.22190/FUME210214031P
+
+"""
+function lmaw(
+    decisionMat::Matrix,
+    weights::Array{Float64,1},
+    fns::Array{F,1};
+    normalization::G = Normalizations.dividebycolumnmaxminnormalization
+)::LMAWResult where {F<:Function, G<:Function}
+
+    row, col = size(decisionMat)
+    w = unitize(weights)
+
+    colMax = colmaxs(decisionMat)
+    colMin = colmins(decisionMat)
+    A = normalization(decisionMat, fns) .+1
+    zerotype = eltype(A)
+    Q = zeros(zerotype, row, col)
+    N = log.(A)
+    a = log.(apply_columns(prod, A))
+    scores = zeros(zerotype, row)
+    for j=1:col
+        N[:,j] = N[:,j]./ a[j]
+        Q[:,j] = (2 .* N[:,j] .^ w[j]) ./ ((2 .- N[:,j]) .^ w[j] + N[:,j] .^ w[j])
+    end
+
+    for i = 1:row
+        scores[i] = sum(Q[i, :])
+    end
+
+    rankings = sortperm(scores)
+
+    bestIndex = rankings |> last
+
+    result = LMAWResult(decisionMat, w, scores, rankings, bestIndex)
+
+    return result
+end
+
+"""
+    lmaw(setting)
+
+Apply LMAW (Logarithm Methodology of Additive Weights) for a given matrix and weights.
+
+# Arguments:
+ - `setting::MCDMSetting`: MCDMSetting object. 
+ 
+# Description 
+lmaw() applies the LMAW method to rank m alternatives subject to n criteria which are supposed to be 
+either maximized or minimized.
+
+# Output 
+- `::LMAWResult`: LMAWResult object that holds multiple outputs including scores, rankings, and best index.
+"""
+function lmaw(setting::MCDMSetting)::LMAWResult
+    lmaw(setting.df, setting.weights, setting.fns)
+end
+
+end # end of module LMAW 

src/mcdm.jl

@@ -29,23 +29,24 @@ julia> subtypes(MCDMMethod)
  EdasMethod
  ElectreMethod
  GreyMethod
+ LMAWMethod
  LOPCOWMethod
- MERECMethod
  MabacMethod
  MaircaMethod
  MarcosMethod
+ MERECMethod
  MooraMethod
  MoosraMethod
  OCRAMethod
  PIVMethod
- PSIMethod
  PrometheeMethod
+ PSIMethod
  ROVMethod
  SawMethod
  TopsisMethod
  VikorMethod
- WPMMethod
  WaspasMethod
+ WPMMethod
 
 
 julia> # mcdm() for Topsis:
@@ -113,6 +114,8 @@ function mcdm(
         lopcow(df, fns)
     elseif method isa OCRAMethod
         ocra(df, w, fns)
+    elseif method isa OCRAMethod
+        lmaw(df, w, fns)
     elseif method isa PIVMethod
         piv(df, w, fns)
     else

test/testmcdm.jl

@@ -1515,4 +1515,37 @@
         @test result2.scores == result.scores
     end
 
+    @testset "LMAW" begin
+        tol = 0.0001
+        decisionMat = [
+            647.34	6.24	49.87	19.46	212.58	6.75;
+            115.64	3.24	16.26	 9.69	207.59	3.00;
+            373.61	5.00	26.43	12.00	184.62	3.74;
+             37.63	2.48	 2.85	 9.25	142.50	3.24;
+            858.01	4.74	62.85	45.96	267.95	4.00;
+            222.92	3.00	19.24	21.46	221.38	3.49
+        ]
+        weights = [0.215, 0.126, 0.152, 0.091, 0.19, 0.226]
+        fns = [maximum, maximum, minimum, minimum, minimum, maximum]
+
+        result = lmaw(decisionMat, weights, fns)
+        @test result isa LMAWResult
+        @test isapprox(
+            result.scores,
+            [
+                4.839005264308832,
+                4.679718180594332,
+                4.797731427991642,
+                4.732145373983716,
+                4.73416833375772,
+                4.702247270959649,
+            ],
+            atol = tol,
+        )
+
+        setting = MCDMSetting(decisionMat, weights, fns)
+        result2 = lmaw(setting)
+        @test result2 isa LMAWResult
+        @test result2.scores == result.scores
+    end
 end