Adjusting Params

belabiedredouane · Oct 31, 2019 · ea36f94 · ea36f94
1 parent 957ddb8
commit ea36f94
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Showing 3 changed files with 27 additions and 15 deletions.
diff --git a/Week 6 - Facility Location/facility/LNS.py b/Week 6 - Facility Location/facility/LNS.py
@@ -341,12 +341,10 @@ def optimize(self):
             print("====================================================")
             if(quantilesCount >= quantileSize):
                 print("Maximum Iteration Count Reached! Stopping...")
-                input("...")
                 break
 
             if(noImprovementIterations > self.params["noImprovementIterationLimit"]):
                 print("No improvement limit reached! Stopping the search...")
-                input("...")
                 break
 
             ##filtrar as facilities mais interessantes e jogar no facility subset

diff --git a/Week 6 - Facility Location/facility/MIP.py b/Week 6 - Facility Location/facility/MIP.py
@@ -39,15 +39,15 @@ def optimize(self,timeLimit):
 
     def __createModelSCIP(self):
         self.model = Model(self.instanceName)
-        print("Creating Variables...")
+        print("SCIP - Creating Variables...")
         #Variables
         for f in self.facilities:
             self.varFacilityAssignment[f.index] = self.model.addVar(vtype="B",name="facility-%s" % f.index)
             for c in self.customers:
                 #Demand is binary because each customer must be served by exaclty one facility
                 self.varCustomerAssignment[f.index,c.index] = self.model.addVar(vtype="B",name="demand-(%s,%s)" % (f.index,c.index))
 
-        print("Creating Constraints...")
+        print("SCIP - Creating Constraints...")
         #Constraints
         #Ensure all customers are assigned to one facility
         for customer in self.customers:
@@ -63,22 +63,22 @@ def __createModelSCIP(self):
                 self.model.addCons(self.varCustomerAssignment[facility.index,customer.index] <=  facility.capacity*self.varFacilityAssignment[facility.index],"Strong(%s,%s)"%(facility.index,customer.index))
                 self.model.addCons(self.varCustomerAssignment[facility.index,customer.index] >= 0 )
 
-        print("Creating Objective Function...")
+        print("SCIP - Creating Objective Function...")
         #Objective Function
         self.model.setObjective(quicksum(self.varFacilityAssignment[facility.index]*facility.setup_cost for facility in self.facilities) + quicksum(Preprocessing.getEuclideanDistance(facility.location,customer.location)*self.varCustomerAssignment[facility.index,customer.index] for facility in self.facilities for customer in self.customers),"minimize")
         self.model.data = self.varFacilityAssignment, self.varCustomerAssignment
 
     def __createModelCPLEX(self):
         self.model = cpx.Model(self.instanceName)
-        print("Creating Variables...")
+        print("CPLEX - Creating Variables...")
         #Variables
         for f in self.facilities:
             self.varFacilityAssignment[f.index] = self.model.binary_var(name="facility-%s" % f.index)
             for c in self.customers:
                 #Demand is binary because each customer must be served by exaclty one facility
                 self.varCustomerAssignment[f.index,c.index] = self.model.binary_var(name="demand-(%s,%s)" % (f.index,c.index))
 
-        print("Creating Constraints...")
+        print("CPLEX - Creating Constraints...")
         #Constraints
         #Ensure all customers are assigned to one facility
         for customer in self.customers:
@@ -94,7 +94,7 @@ def __createModelCPLEX(self):
                 self.model.add_constraint(ct=self.varCustomerAssignment[facility.index,customer.index] <=  facility.capacity*self.varFacilityAssignment[facility.index],ctname="Strong(%s,%s)"%(facility.index,customer.index))
                 self.model.add_constraint(self.varCustomerAssignment[facility.index,customer.index] >= 0,ctname="Strong2(%s,%s)"%(facility.index,customer.index))
 
-        print("Creating Objective Function...")
+        print("CLPEX - Creating Objective Function...")
         #Objective Function
         objective = self.model.sum(self.varFacilityAssignment[facility.index]*facility.setup_cost for facility in self.facilities) + self.model.sum(Preprocessing.getEuclideanDistance(facility.location,customer.location)*self.varCustomerAssignment[facility.index,customer.index] for facility in self.facilities for customer in self.customers)
         self.model.minimize(objective)
@@ -108,7 +108,7 @@ def __optimizeCPLEX(self,timeLimit):
         self.model.parameters.timelimit = timeLimit
         self.model.parameters.threads = 8
 
-        print("MIP - Optimizing...")
+        print("CPLEX - Optimizing...")
         solution = self.model.solve(log_output=self.DEBUG_MESSAGES)
 
         if solution is not None:
@@ -138,7 +138,7 @@ def __optimizeSCIP(self,timeLimit):
 
         self.model.setRealParam('limits/time', timeLimit)
 
-        print("MIP - Optimizing...")
+        print("SCIP - Optimizing...")
         self.model.optimize()
         print("Instace: %s solved." % self.instanceName)
         EPS = 1.e-6

diff --git a/Week 6 - Facility Location/facility/ParametersConfiguration.py b/Week 6 - Facility Location/facility/ParametersConfiguration.py
@@ -4,8 +4,7 @@
 import datetime
 
 class ParametersConfiguration:
-    INITIAL_FACILITIES_BY_SUBPROBLEM = 5  #Maximum desired number of facilities 'in' the first cluster
-
+
     def __init__(self,facilityCount,instanceSize):
         self.instanceSize = instanceSize
         self.facilityCount = facilityCount
@@ -14,7 +13,7 @@ def __init__(self,facilityCount,instanceSize):
 
 
     def __getQuantilesIntervals(self):
-        firstQuantile = Util.truncate(self.INITIAL_FACILITIES_BY_SUBPROBLEM/float(self.facilityCount),10) 
+        firstQuantile = Util.truncate(self.params["initial_facilities_subproblem"]/float(self.facilityCount),10) 
         if(firstQuantile > 1.0):
             firstQuantile = 1
         quantileIntervals = round(1.0/firstQuantile)
@@ -39,14 +38,18 @@ def __setStrategyParameters(self,instanceSize):
         if(self.params is None): 
             if (instanceSize <= 50000): 
                 self.__getInstanceParameters(Strategy.Alpha,instanceSize)
-            else:
+            elif(instanceSize <= 100000):
                 self.__getInstanceParameters(Strategy.Beta,instanceSize)
+            else:
+                self.__getInstanceParameters(Strategy.Delta,instanceSize)
 
     def __getInstanceParameters(self,strategy,instanceSize):  
         if strategy == Strategy.Alpha:
             self.__AlphaSetup(instanceSize)
         elif strategy == Strategy.Beta:
             self.__BetaSetup(instanceSize)
+        elif strategy == Strategy.Delta:
+            self.__DeltaSetup(instanceSize)
 
     def __DefaultSetup(self,instanceSize):
         self.params = {}
@@ -56,18 +59,29 @@ def __DefaultSetup(self,instanceSize):
         self.params["mipTimeLimit"] = Util.getTimeInSeconds(4,0,0) 
         self.params["strategy"] = Strategy.Default
         self.params["paradigm"] = SolvingParadigm.MIP
-        self.params["quantile_intervals"] = self.__getQuantilesIntervals()
+        self.params["initial_facilities_subproblem"] = 5 #Maximum desired number of facilities 'in' the first cluster
         self.params["initialSolutionFunction"] = InitialSolutionFunction.Euclidean
         self.params["mipSolver"] = MipSolver.CPLEX
+
 
     def __AlphaSetup(self,instanceSize):
         self.__DefaultSetup(instanceSize)
         self.params["strategy"] = Strategy.Alpha
         self.params["paradigm"] = SolvingParadigm.MIP
+        self.params["quantile_intervals"] = self.__getQuantilesIntervals()
 
     def __BetaSetup(self,instanceSize):
         self.__DefaultSetup(instanceSize)
         self.params["strategy"] = Strategy.Beta
         self.params["paradigm"] = SolvingParadigm.Hybrid
         self.params["improvementType"] = ImprovementType.First
+        self.params["quantile_intervals"] = self.__getQuantilesIntervals()
+
+    def __DeltaSetup(self,instanceSize):
+        self.__DefaultSetup(instanceSize)
+        self.params["strategy"] = Strategy.Delta
+        self.params["paradigm"] = SolvingParadigm.Hybrid
+        self.params["improvementType"] = ImprovementType.First
+        self.params["initial_facilities_subproblem"] = 10
+        self.params["quantile_intervals"] = self.__getQuantilesIntervals()