update p-median for predefined distance matrix

docs/src/algorithms.md

@@ -68,6 +68,10 @@ julia> result.flow
 OperationsResearchModels.pmedian
 ```
 
+```@docs 
+OperationsResearchModels.pmedian_with_distances
+```
+
 ## Minimum Spanning Tree
 ```@docs
 OperationsResearchModels.mst

src/OperationsResearchModels.jl

@@ -40,7 +40,7 @@ import .MaximumFlow: MaximumFlowResult
 import .Assignment: AssignmentProblem, AssignmentResult
 import .Game: game, GameResult
 import .MinimumSpanningTree: hasloop, mst, MstResult
-import .PMedian: pmedian
+import .PMedian: pmedian, pmedian_with_distances
 import .CPM: cpm, CpmActivity, earliestfinishtime, longestactivity, CpmResult
 import .CPM: pert, PertActivity, PertResult
 import .Latex: latex
@@ -51,7 +51,7 @@ export ShortestPathProblem, MaximumFlowProblem
 export AssignmentProblem, AssignmentResult
 export game, GameResult
 export hasloop, mst, MstResult
-export pmedian
+export pmedian, pmedian_with_distances
 export cpm, CpmActivity, earliestfinishtime, longestactivity, CpmResult
 export pert, PertActivity, PertResult
 export Simplex

src/pmedian.jl

@@ -60,6 +60,32 @@ function pmedian(data::Matrix, ncenters::Int)
         end
     end
 
+    return pmedian_with_distances(distances, ncenters)
+end
+
+
+
+"""
+
+    pmedian_with_distances(distancematrix, ncenters)
+
+# Arguments 
+- `distancematrix::Matrix`: n x n matrix of distances
+- `ncenters::Int`: Number of centers 
+
+# Descriptions 
+`ncenters` locations are selected that minimizes the total distances to the nearest rows. 
+
+# Output 
+- `Dict{String, Any}`: The dictionary object that holds the results. 
+"""
+function pmedian_with_distances(distancematrix::Matrix, ncenters::Int)
+
+    n, _ = size(distancematrix)
+
+    distances = distancematrix
+
+
     model = Model(HiGHS.Optimizer)
     MOI.set(model, MOI.Silent(), true)
 

test/testpmedian.jl

@@ -22,4 +22,16 @@
         @test iszero(y[5])
         @test isone(y[6])
     end
+
+    @testset "p-median with distances" begin 
+        distance_matrix = Float64[0 6 3 2; 6 0 4 7; 3 4 0 10; 2 7 10 0]
+
+        number_of_depots = 2
+
+        result = pmedian_with_distances(distance_matrix, number_of_depots)
+
+        @test sort(result["centers"]) == [1, 2]
+        @test result["objective"] == 5.0
+        @test result["z"] ≈ [1 0 0 0; 0 1 0 0; 1 0 0 0; 1 0 0 0]
+    end 
 end