diff --git a/docs/src/mcdm.md b/docs/src/mcdm.md
index 1087096a..110e7cbc 100644
--- a/docs/src/mcdm.md
+++ b/docs/src/mcdm.md
@@ -88,8 +88,6 @@ more details about the methods.
 Performing MCDM using a population.
 
 ```julia-repl
-julia> using JMcDM
-
 julia> _, _, population = Metaheuristics.TestProblems.ZDT1();
 
 julia> dm = mcdm(population, [0.5, 0.5], TopsisMethod());
@@ -103,8 +101,6 @@ julia> population[dm.bestIndex]
 Performing MCDM using results from metaheuristic.
 
 ```julia-repl
-julia> using JMcDM
-
 julia> f, bounds, _ = Metaheuristics.TestProblems.ZDT1();
 
 julia> res = optimize(f, bounds, NSGA2());
diff --git a/src/DecisionMaking/DecisionMaking.jl b/src/DecisionMaking/DecisionMaking.jl
index 4e1a769a..81c8a91e 100644
--- a/src/DecisionMaking/DecisionMaking.jl
+++ b/src/DecisionMaking/DecisionMaking.jl
@@ -5,7 +5,7 @@ include("ROI.jl")
 include("CompromiseProgramming.jl")
 
 export decisionmaking, dm, best_alternative, ROIArchiving, CompromiseProgramming
-export WeightedSum, Tchebysheff, AchievementScalarization
+export WeightedSum, Tchebysheff, AchievementScalarization, summary
 
 """
     decisionmaking(fs, w, method)
diff --git a/src/DecisionMaking/JMcDM.jl b/src/DecisionMaking/JMcDM.jl
index 204953d8..12356927 100644
--- a/src/DecisionMaking/JMcDM.jl
+++ b/src/DecisionMaking/JMcDM.jl
@@ -10,6 +10,13 @@ export summary
 export best_alternative
 
 
+export ArasMethod, CocosoMethod, CodasMethod, CoprasMethod 
+export EdasMethod, ElectreMethod, GreyMethod, MabacMethod, MaircaMethod
+export MooraMethod, SawMethod, TopsisMethod, VikorMethod, WPMMethod
+export WaspasMethod, MarcosMethod
+export DataFrame
+
+
 """
     mcdm(fs, w, method)
 
diff --git a/src/PerformanceIndicators/hypervolume.jl b/src/PerformanceIndicators/hypervolume.jl
index fdf09e17..19b9ef88 100644
--- a/src/PerformanceIndicators/hypervolume.jl
+++ b/src/PerformanceIndicators/hypervolume.jl
@@ -164,7 +164,7 @@ is a non-dominated set.
 If `front::State` and `reference_point::Vector`, then computes `hypervolume(front.population, reference_point)` after
 ignoring solutions in `front` that do not dominate `reference_point`.
 """
-function hypervolume(front::Array{Vector{T}}, reference_point::Vector) where T <: Real
+function hypervolume(front::Array{Vector{T}}, reference_point::Vector; verbose=true) where T <: Real
 
 	weaklyDominates(point, other) = begin
 		for i in 1:length(point)
@@ -187,16 +187,16 @@ function hypervolume(front::Array{Vector{T}}, reference_point::Vector) where T <
 	if length(relevantPoints) != length(front)
 		ign = length(front) - length(relevantPoints)
 		rel = length(relevantPoints)
-		@warn "Ignoring $ign points dominated by the reference point ($rel points are used)."
+		verbose && @warn "Ignoring $ign points dominated by the reference point ($rel points are used)."
 	end
 
 	return HyperVolume.hv(relevantPoints, reference_point)
 end
 
-hypervolume(front::Vector{T}, reference_point::Vector{T}) where T <: AbstractMultiObjectiveSolution = hypervolume(fval.(front), fval(reference_point))
-hypervolume(front::Vector{T}, reference_point::Vector) where T <: AbstractMultiObjectiveSolution = hypervolume(fval.(front), reference_point)
+hypervolume(front::Vector{T}, reference_point::Vector{T}; verbose=true) where T <: AbstractMultiObjectiveSolution = hypervolume(fval.(front), fval(reference_point); verbose)
+hypervolume(front::Vector{T}, reference_point::Vector; verbose=true) where T <: AbstractMultiObjectiveSolution = hypervolume(fval.(front), reference_point;verbose)
 
-function hypervolume(front::Matrix, reference_point::Vector)
+function hypervolume(front::Matrix, reference_point::Vector; verbose=true)
 	front_ = [ front[i,:] for i in 1:size(front,1) ]
-	hypervolume(front_, reference_point)
+	hypervolume(front_, reference_point; verbose)
 end
diff --git a/test/common-methods.jl b/test/common-methods.jl
index 430a305d..424a2d51 100644
--- a/test/common-methods.jl
+++ b/test/common-methods.jl
@@ -129,7 +129,7 @@ import Random: seed!
                  [1.1       ,0.        ,0.        ]]
 
         referencePoint = [2.0, 2, 2]
-        hyperVolume = Metaheuristics.PerformanceIndicators.hypervolume(front, referencePoint)
+        hyperVolume = Metaheuristics.PerformanceIndicators.hypervolume(front, referencePoint, verbose=false)
         @test hyperVolume ≈ 6.793879034744429
 
         front_ = Array(hcat(front...)')
diff --git a/test/decisionmaking.jl b/test/decisionmaking.jl
index a9accab8..dfe56e28 100644
--- a/test/decisionmaking.jl
+++ b/test/decisionmaking.jl
@@ -1,11 +1,3 @@
-import Metaheuristics.JMcDM
-import Metaheuristics.JMcDM: ArasMethod, CocosoMethod, CodasMethod, CoprasMethod 
-import Metaheuristics.JMcDM: EdasMethod, ElectreMethod, GreyMethod, MabacMethod, MaircaMethod
-import Metaheuristics.JMcDM: MooraMethod, SawMethod, TopsisMethod, VikorMethod, WPMMethod
-import Metaheuristics.JMcDM: WaspasMethod, MarcosMethod
-import Metaheuristics.JMcDM: DataFrame
-
-
 @testset "DecisionMaking: Compromise Programming" begin
     function test_cp()
         _, _, pf = Metaheuristics.TestProblems.ZDT1();
@@ -52,8 +44,6 @@ end
 
         w = [0.5, 0.5]
 
-        # PrometheeMethod
-        # PSIMethod, MoosraMethod --err
         methods = [
                    ArasMethod, CocosoMethod, CodasMethod, CoprasMethod, 
                    EdasMethod, ElectreMethod, GreyMethod, MabacMethod, MaircaMethod,
@@ -62,8 +52,8 @@ end
                   ]
 
         for method in methods
-            res_dm = JMcDM.mcdm(res, w, method())
-            res_dm2 = JMcDM.mcdm(MCDMSetting(res, w), method())
+            res_dm = mcdm(res, w, method())
+            res_dm2 = mcdm(MCDMSetting(res, w), method())
             @test res_dm.bestIndex == res_dm2.bestIndex
             
             # bestIndex can be touple and needs to be handled...
@@ -74,7 +64,7 @@ end
             @test Metaheuristics.compare(fval(best_sol), fval(ref_sol)) == 0
         end
 
-        @test JMcDM.summary(res, w, [:topsis, :electre, :vikor]) isa DataFrame
+        @test Metaheuristics.summary(res, w, [:topsis, :electre, :vikor]) isa DataFrame
 
         # check default method TopsisMethod
         @test mcdm(MCDMSetting(res, w)).bestIndex == mcdm(res, w, TopsisMethod()).bestIndex