(#66) Implement SPOTIS method

Author: jbytecode

CHANGELOG.md

@@ -1,7 +1,7 @@
 ### 0.8.4 (Upcoming release)
 
 - Simplify code and documentation of CRADIS method.
-
+- Implement SPOTIS method
 
 ### 0.8.3
 

docs/src/mcdms.md

@@ -207,4 +207,10 @@ JMcDM.ram
 ## CRADIS
 ```@docs
 JMcDM.cradis    
-```
+```
+
+## SPOTIS
+```@docs
+JMcDM.spotis    
+```
+

src/JMcDM.jl

@@ -120,6 +120,7 @@ include("lopcow.jl")
 include("ocra.jl")
 include("lmaw.jl")
 include("todim.jl")
+include("spotis.jl")
 
 include("summary.jl")
 
@@ -140,6 +141,7 @@ include("fuzzy/fuzzy.jl")
 
 # imports from modules
 import .Topsis: topsis, TopsisMethod, TopsisResult
+import .SPOTIS: spotis, SpotisMethod, SPOTISResult
 import .WPM: wpm, WPMResult, WPMMethod
 import .WASPAS: waspas, WASPASResult, WaspasMethod
 import .VIKOR: vikor, VikorMethod, VikorResult
@@ -222,6 +224,7 @@ export LMAWMethod
 export TODIMMethod
 export RAMMethod
 export CradisMethod
+export SpotisMethod
 
 
 export MCDMSetting
@@ -262,6 +265,7 @@ export LMAWResult
 export TODIMResult
 export CILOSResult
 export IDOCRIWResult
+export SPOTISResult
 export RAMResult
 export CRADISResult
 
@@ -325,6 +329,7 @@ export idocriw
 export ram
 export borda
 export cradis
+export spotis
 
 #  export SCDM tools
 export laplace

src/mcdm.jl

@@ -118,6 +118,8 @@ function mcdm(
         lmaw(df, w, fns)
     elseif method isa PIVMethod
         piv(df, w, fns)
+    elseif method isa SpotisMethod
+        spotis(df, w, fns, lowerbounds=method.lowerbounds, upperbounds=method.upperbounds)
     else
         error("Method is not defined")
     end

src/spotis.jl

@@ -0,0 +1,138 @@
+module SPOTIS
+
+
+import ..MCDMMethod, ..MCDMResult, ..MCDMSetting
+import ..Normalizations
+
+using ..Utilities: weightise, colmins, colmaxs, rowsums, unitize
+
+export spotis, SpotisMethod, SPOTISResult
+
+struct SpotisMethod <: MCDMMethod
+    lowerbounds::Vector{Float64}
+    upperbounds::Vector{Float64}    
+end
+
+struct SPOTISResult <: MCDMResult
+    decisionMat::Matrix
+    normalizedDecisionMat::Matrix
+    weights::Vector{Float64}
+    weightedNormalizedDecisionMat::Matrix{Float64}
+    scores::Vector{Float64}
+    ranking::Vector{Int}
+    bestIndex::Int
+end
+
+
+"""
+    spotis(decisionMat, weights, dirs; lowerbounds=nothing, upperbounds=nothing)
+
+# Description
+
+Implements the SPOTIS Rank Reversal Free Method for Multi-Criteria Decision-Making Support
+
+# Arguments 
+
+- `decisionMat::Matrix`: The decision matrix in type of Matrix.
+- `weights::Array{Float64,1}`: Array of weights for each criterion.
+- `dirs::Array{<:Function,1}`: Array of functions. The elements are either minimum or maximum.
+- `lowerbounds::Union{Nothing, Array{Float64,1}}`: Optional array of lower bounds for each criterion. If not provided, the minimum values of the decision matrix will be used.
+- `upperbounds::Union{Nothing, Array{Float64,1}}`: Optional array of upper bounds for each criterion. If not provided, the maximum values of the decision matrix will be used.
+
+# Reference:
+
+- Dezert, Jean, et al. "The SPOTIS rank reversal free method for multi-criteria decision-making support." 2020 IEEE 23rd international conference on information fusion (FUSION). IEEE, 2020.
+"""
+function spotis(
+    decisionMat::Matrix,
+    weights::Array{Float64,1},
+    dirs::Array{F,1};
+    lowerbounds::Union{Nothing, Array{Float64,1}}=nothing,
+    upperbounds::Union{Nothing, Array{Float64,1}}=nothing)::SPOTISResult where {F<:Function}
+
+    nalternatives, ncriteria = size(decisionMat)
+
+    if length(weights) != ncriteria
+        error("Length of weights must be equal to number of criteria")
+    end
+
+    if length(dirs) != ncriteria
+        error("Length of dirs must be equal to number of criteria")
+    end
+
+    w = unitize(weights)
+
+    if lowerbounds === nothing
+        lowerbounds = colmins(decisionMat)
+    end
+    if upperbounds === nothing
+        upperbounds = colmaxs(decisionMat)
+    end
+
+    if length(lowerbounds) != ncriteria || length(upperbounds) != ncriteria
+        error("Length of lowerbounds/upperbounds must be equal to number of criteria")
+    end
+
+    normalizedDecisionMat = Array{Float64}(undef, nalternatives, ncriteria)
+
+    @inbounds for j = 1:ncriteria
+        lower = lowerbounds[j]
+        upper = upperbounds[j]
+
+        if upper < lower
+            error("upperbounds[$j] must be greater than or equal to lowerbounds[$j]")
+        end
+
+        ideal = if dirs[j] == minimum
+            lower
+        elseif dirs[j] == maximum
+            upper
+        else
+            error("Each function in dirs must be either minimum or maximum")
+        end
+
+        span = upper - lower
+        for i = 1:nalternatives
+            normalizedDecisionMat[i, j] = if iszero(span)
+                0.0
+            else
+                abs(decisionMat[i, j] - ideal) / span
+            end
+        end
+    end
+
+    weightedNormalizedDecisionMat = weightise(normalizedDecisionMat, w)
+    scores = rowsums(weightedNormalizedDecisionMat)
+    ranking = sortperm(scores)
+    bestIndex = first(ranking)
+
+    return SPOTISResult(
+        decisionMat,
+        normalizedDecisionMat,
+        w,
+        weightedNormalizedDecisionMat,
+        scores,
+        ranking,
+        bestIndex)
+end
+
+
+
+function spotis(
+    setting::MCDMSetting;
+    lowerbounds::Union{Nothing, Array{Float64,1}}=nothing,
+    upperbounds::Union{Nothing, Array{Float64,1}}=nothing)::SPOTISResult
+
+    return spotis(
+        setting.df,
+        setting.weights,
+        setting.fns,
+        lowerbounds=lowerbounds,
+        upperbounds=upperbounds,
+    )
+end
+
+
+
+
+end # end of module SPOTIS

test/mcdm/testspotis.jl

@@ -0,0 +1,44 @@
+@testset "SPOTIS" begin
+    tol = 0.0002
+
+    # Example extracted from "The SPOTIS Rank Reversal Free Method" (Example 2, car selection).
+    decmat = [
+        15000.0 4.3 99.0 42.0 737.0
+        15290.0 5.0 116.0 42.0 892.0
+        15350.0 5.0 114.0 45.0 952.0
+        15490.0 5.3 123.0 45.0 1120.0
+    ]
+
+    # Importance values [5, 4, 4, 1, 3] normalized internally by the method.
+    raw_weights = [5.0, 4.0, 4.0, 1.0, 3.0]
+    weights = raw_weights ./ sum(raw_weights)
+
+    # C1, C2, C3 are cost criteria. C4, C5 are benefit criteria.
+    dirs = [minimum, minimum, minimum, maximum, maximum]
+
+    lowerbounds = [14000.0, 3.0, 80.0, 35.0, 650.0]
+    upperbounds = [16000.0, 8.0, 140.0, 60.0, 1300.0]
+
+    result = spotis(decmat, weights, dirs, lowerbounds=lowerbounds, upperbounds=upperbounds)
+
+    @test result isa SPOTISResult
+    @test isapprox(result.scores, [0.4779, 0.5781, 0.5558, 0.5801], atol = tol)
+    @test result.ranking == [1, 3, 2, 4]
+    @test result.bestIndex == 1
+
+    setting = MCDMSetting(decmat, weights, dirs)
+    result2 = spotis(setting, lowerbounds=lowerbounds, upperbounds=upperbounds)
+
+    @test result2 isa SPOTISResult
+    @test result2.bestIndex == result.bestIndex
+    @test result2.ranking == result.ranking
+    @test isapprox(result2.scores, result.scores, atol = tol)
+
+    method = SpotisMethod(lowerbounds, upperbounds)
+    result3 = mcdm(setting, method)
+
+    @test result3 isa SPOTISResult
+    @test result3.bestIndex == result.bestIndex
+    @test result3.ranking == result.ranking
+    @test isapprox(result3.scores, result.scores, atol = tol)
+end

test/testmcdm.jl

@@ -11,6 +11,7 @@ include("mcdm/testrov.jl")
 include("mcdm/testsd.jl")
 include("mcdm/testgra.jl")
 include("mcdm/testtopsis.jl")
+include("mcdm/testspotis.jl")
 include("mcdm/testvikor.jl")
 include("mcdm/testelectre.jl")
 include("mcdm/testmoora.jl")