implement PSI (Preference Selection Index) method

CHANGELOG.md

@@ -1,3 +1,6 @@
+### 0.3.9
+- Implement PSI (Preference Selection Index) method
+
 ### 0.3.8
 - Small bug fixes 
 - Remove the critic method from summary()

Project.toml

@@ -1,7 +1,7 @@
 name = "JMcDM"
 uuid = "358108f5-d052-4d0a-8344-d5384e00c0e5"
 authors = ["Mehmet Hakan Satman (jbytecode) <[email protected]>", "Bahadir Fatih Yildirim <[email protected]>", "Ersagun Kuruca"]
-version = "0.3.8"
+version = "0.3.9"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

README.md

@@ -94,6 +94,7 @@ Please check out the reference manual [here](https://jbytecode.github.io/JMcDM/d
 - Copeland (For combining multiple ordering results)
 - SD Method for determining weights of criteria
 - ROV (Range of Value) Method
+- PSI (Preference Selection Index) Method
 
 ### SCDM Tools
 

src/JMcDM.jl

@@ -38,6 +38,7 @@ include("cocoso.jl")
 include("critic.jl")
 include("entropy.jl")
 include("codas.jl")
+include("psi.jl")
 
 include("summary.jl")
 
@@ -72,6 +73,7 @@ export VikorMethod
 export WPMMethod
 export WaspasMethod
 export MarcosMethod
+export PSIMethod
 
 export MCDMSetting
 
@@ -101,6 +103,7 @@ export EntropyResult
 export CODASResult
 export SDResult
 export ROVResult
+export PSIResult
 
 # export game type
 export GameResult
@@ -155,6 +158,7 @@ export entropy
 export codas
 export sd
 export rov
+export psi
 
 #  export SCDM tools
 export laplace

src/print.jl

@@ -22,7 +22,7 @@ end
 function Base.show(io::IO, result::CODASResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -32,14 +32,14 @@ end
 function Base.show(io::IO, result::COPRASResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
 end
 
 function Base.show(io::IO, result::CRITICResult)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -48,7 +48,7 @@ end
 function Base.show(io::IO, result::CoCoSoResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -57,7 +57,7 @@ end
 function Base.show(io::IO, result::EDASResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -80,7 +80,7 @@ end
 function Base.show(io::IO, result::MABACResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -89,7 +89,7 @@ end
 function Base.show(io::IO, result::MAIRCAResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -98,7 +98,7 @@ end
 function Base.show(io::IO, result::MARCOSResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -114,7 +114,7 @@ end
 function Base.show(io::IO, result::PrometheeResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -123,7 +123,7 @@ end
 function Base.show(io::IO, result::SawResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -139,7 +139,7 @@ end
 function Base.show(io::IO, result::WASPASResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -148,7 +148,7 @@ end
 function Base.show(io::IO, result::WPMResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranking)
     println(io, "Best indices:")
     println(io, result.bestIndex)
@@ -157,14 +157,22 @@ end
 function Base.show(io::IO, result::ROVResult)
     println(io, "Scores:")
     println(io, result.scores)
-    println(io, "Ranking: ")
+    println(io, "Ordering: ")
     println(io, result.ranks)
     println(io, "Uminus:")
     println(io, result.uminus)
     println(io, "UPlus:")
     println(io, result.uplus)
 end
 
+function Base.show(io::IO, result::PSIResult)
+    println(io, "Scores:")
+    println(io, result.scores)
+    println(io, "Ordering: (from worst to best)")
+    println(io, result.rankings)
+    println(io, "Best indices:")
+    println(io, result.bestIndex)
+end
 
 
 

src/psi.jl

@@ -0,0 +1,131 @@
+"""
+        psi(decisionMat, fns)
+
+Apply PSI (Preference Selection Index) method for a given matrix and directions of optimizations.
+
+# Arguments:
+ - `decisionMat::DataFrame`: n × m matrix of objective values for n alterntives and m criteria 
+ - `fns::Array{Function, 1}`: m-vector of functions to be applied on the columns.
+ 
+# Description 
+psi() applies the PSI method to rank n alterntives subject to m criteria which are supposed to be 
+either maximized or minimized.
+
+# Output 
+- `::PSIResult`: PSIResult object that holds multiple outputs including scores, rankings, and best index.
+
+# Examples
+```julia-repl
+julia> decmat = [3        12.5        2        120        14        3;
+       5        15        3        110        38        4;
+       3        13        2        120        19        3;
+       4        14        2        100        31        4;
+       3        15        1.5        125        40        4]
+5×6 Array{Float64,2}:
+ 3.0  12.5  2.0  120.0  14.0  3.0
+ 5.0  15.0  3.0  110.0  38.0  4.0
+ 3.0  13.0  2.0  120.0  19.0  3.0
+ 4.0  14.0  2.0  100.0  31.0  4.0
+ 3.0  15.0  1.5  125.0  40.0  4.0
+
+julia> df = makeDecisionMatrix(decmat)
+5×6 DataFrame
+ Row │ Crt1     Crt2     Crt3     Crt4     Crt5     Crt6    
+     │ Float64  Float64  Float64  Float64  Float64  Float64 
+─────┼──────────────────────────────────────────────────────
+   1 │     3.0     12.5      2.0    120.0     14.0      3.0
+   2 │     5.0     15.0      3.0    110.0     38.0      4.0
+   3 │     3.0     13.0      2.0    120.0     19.0      3.0
+   4 │     4.0     14.0      2.0    100.0     31.0      4.0
+   5 │     3.0     15.0      1.5    125.0     40.0      4.0
+
+
+julia> fns = [maximum, minimum, minimum, maximum, minimum, maximum];
+
+julia> result = psi(df, fns)
+Scores:
+[1.1252480520930113, 0.762593438114615, 1.1060476892230147, 1.0059872302387025, 0.7865885089329105]
+Ordering: (from worst to best)
+[2, 5, 4, 3, 1]
+Best indices:
+1
+
+julia> result.bestIndex 
+1
+```
+
+# References
+Maniya, Kalpesh, and Mangal Guido Bhatt. "A selection of material using a novel type decision-making method: 
+Preference selection index method." Materials & Design 31.4 (2010): 1785-1789
+"""
+function psi(decisionMat::DataFrame, fns::Array{Function,1})::PSIResult
+
+    function PV(v)
+        mymean = mean(v)
+        meandiff = v .- mymean
+        meandiffsq = meandiff .* meandiff 
+        return sum(meandiffsq)
+    end
+
+    row, col = size(decisionMat)
+    normalizedDecisionMat = similar(decisionMat)
+    colminmax = zeros(Float64, col)
+    @inbounds for i in 1:col
+        colminmax[i] = decisionMat[:, i] |> fns[i]
+        if fns[i] == maximum
+            normalizedDecisionMat[:, i] = decisionMat[:, i] ./ colminmax[i] 
+        elseif fns[i] == minimum 
+            normalizedDecisionMat[:, i] = colminmax[i] ./ decisionMat[:, i]
+        end
+    end    
+
+    pvs = zeros(Float64, row)
+    for i in 1:row 
+        pvs[i] = PV(normalizedDecisionMat[i, :] |> collect)
+    end 
+
+    phis = 1.0 .- pvs 
+    sum_phis = sum(phis)
+
+    psis = phis ./ sum_phis
+
+    Is = zeros(Float64, row)
+    for i in 1:row
+        Is[i] = psis[i] .* collect(normalizedDecisionMat[i, :]) |> sum
+    end
+
+    scores = Is 
+    ranks = sortperm(scores)
+    bestindex = ranks |> last  
+
+    result = PSIResult(
+        scores,
+        ranks,
+        bestindex
+    )
+
+    return result
+end
+
+
+"""
+        psi(setting)
+
+Apply PSI (Preference Selection Index) method for a given matrix and weights.
+
+# Arguments:
+ - `setting::MCDMSetting`: MCDMSetting object. 
+
+# Description 
+psi() applies the PSI method to rank n alterntives subject to m criteria which are supposed to be 
+either maximized or minimized.
+
+# Output 
+- `::PSIResult`: PSIResult object that holds multiple outputs including scores, rankings, and best index.
+"""
+function psi(setting::MCDMSetting)::PSIResult
+    psi(
+        setting.df,
+        setting.fns
+    )
+end 

src/types.jl

@@ -50,6 +50,12 @@ struct MCDMSetting
     fns::Array{Function, 1}
 end
 
+struct PSIResult <: MCDMResult
+    scores::Array{Float64,1}
+    rankings::Array{Int, 1}
+    bestIndex::Int
+end
+
 struct TopsisResult <: MCDMResult
     decisionMatrix::DataFrame
     weights::Array{Float64,1}
@@ -377,6 +383,9 @@ end
 struct MaircaMethod <: MCDMMethod 
 end 
 
+struct PSIMethod <: MCDMMethod
+end
+
 
 struct MooraMethod <: MCDMMethod 
     method::Symbol

test.jl

@@ -0,0 +1,18 @@
+using JMcDM
+using DataFrames
+
+
+
+
+df = DataFrame()
+df[:, :x] = Float64[9, 8, 7]
+df[:, :y] = Float64[7, 7, 8]
+df[:, :z] = Float64[6, 9, 6]
+df[:, :q] = Float64[7, 6, 6]
+w = Float64[4, 2, 6, 8]
+
+fns = makeminmax([maximum, maximum, maximum, maximum])
+
+result = psi(df, fns)
+
+

test/testmcdm.jl

@@ -1,5 +1,28 @@
 @testset "MCDM functions" begin
 
+    @testset "PSI" begin 
+        tol = 0.0001
+
+        df = DataFrame()
+        df[:, :x] = Float64[9, 8, 7]
+        df[:, :y] = Float64[7, 7, 8]
+        df[:, :z] = Float64[6, 9, 6]
+        df[:, :q] = Float64[7, 6, 6]
+        w = Float64[4, 2, 6, 8]
+
+        fns = makeminmax([maximum, maximum, maximum, maximum])
+
+        result = psi(df, fns)
+
+        @test result isa PSIResult
+        @test result.bestIndex == 2
+        @test isapprox(
+            [1.1487059780663555, 1.252775986851622, 1.0884916686098811],
+            result.scores,
+            atol = tol
+        )
+    end
+
     @testset "ROV" begin
         tol = 0.01
         mat = [