progression to new LP

dream/simulation/SkilledOperatorRouter.py

@@ -173,7 +173,7 @@ class SkilledRouter(Router):
                 #===================================================================
                 self.availableStationsDict={}
                 for station in self.availableStations:
-                    self.availableStationsDict[str(station.id)]={'stationID':str(station.id),'WIP':station.wip}
+                    self.availableStationsDict[str(station.id)]={'stationID':str(station.id),'WIP':station.wip, 'lastAssignment':self.env.now}
                 #===================================================================
                 # # operators and their skills set
                 #===================================================================

dream/simulation/opAss_LPmethod.py

@@ -4,7 +4,7 @@ Created on 2 Jul 2014
 @author: Anna
 '''
 
-def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
+def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}, weightFactors = [2, 1, 0.5, 2, 1, 1], Tool={}):
     
     from pulp import LpProblem, LpMaximize, LpVariable, LpBinary, lpSum, LpStatus
     import pulp
@@ -14,25 +14,27 @@ def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
 
     machines = machineList.keys()
     sumWIP = float(sum([machineList[mach]['WIP'] for mach in machines ]))
-    weightFactors = [2, 1, 0.5, 1.5]
         
     # define LP problem    
     prob = LpProblem("PBassignment", LpMaximize)
+    obj = []
         
     # declare variables...binary assignment variables (operator i to machine j)
     PB_ass = LpVariable.dicts('PB', [(oper,mach) for oper in PBlist for mach in machines if machineList[mach]['stationID'] in PBskills[oper]] , 0, 1, cat=pulp.LpBinary)
     
     # objective...assignment of PBs to stations with higher WIP...sum of WIP associated with stations where PB is assigned
-    obj = [machineList[mach]['WIP']*PB_ass[(oper,mach)]*weightFactors[0]/float(sumWIP) for oper in PBlist for mach in machines if machineList[mach]['stationID'] in PBskills[oper]]
+    if weightFactors[0]>0 and sumWIP>0:
+        obj.append([machineList[mach]['WIP']*PB_ass[(oper,mach)]*weightFactors[0]/float(sumWIP) for oper in PBlist for mach in machines if machineList[mach]['stationID'] in PBskills[oper]])
     
     
     # second set of variables (delta assignment between stations) to facilitate the distribution of PBs across different stations
+    if weightFactors[1]>0:
         stationGroup = {}
         for mach in machines:
             if machineList[mach]['stationID'] not in stationGroup:
                 stationGroup[machineList[mach]['stationID']] = []
             stationGroup[machineList[mach]['stationID']].append(mach)
-    Delta_Station = LpVariable.dicts("D_station",[(st1,st2) for i1, st1 in enumerate(stationGroup.keys()) for st2 in stationGroup.keys()[i1+1:]])
+        Delta_Station = LpVariable.dicts("D_station", [(st1, st2) for i1, st1 in enumerate(stationGroup.keys()) for st2 in stationGroup.keys()[i1 + 1:]])
     
         # calculate global max number of machines within a station that will be used as dividers for Delta_Station  
         maxNoMachines = 0
@@ -68,6 +70,7 @@ def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
                 obj.append(Delta_Station[(st1,st2)]*weightFactors[1]/float(normalisingFactorDeltaStation) )
         
     # min variation in PB assignment
+    if weightFactors[2]>0:
         Delta_Assignment = []
         OldAss = {}
         for pb in previousAssignment:
@@ -98,6 +101,7 @@ def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
             
             
     # 4th obj = fill a subline
+    if weightFactors[3]>0:
         # verify whether there are machines active in the sublines
         subline={0:{'noMach':0, 'WIP':0}, 1:{'noMach':0, 'WIP':0}}
         for mach in machineList:
@@ -127,7 +131,7 @@ def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
             subLine = LpVariable('SubL', lowBound=0)
             sub = []
             for station in range(3):
-            mach = G.Tool[station][chosenSubLine].name        #'St'+str(station)+'_M'+str(chosenSubLine)
+                mach = Tool[station][chosenSubLine].name        #'St'+str(station)+'_M'+str(chosenSubLine)
                 for oper in PBlist:
                     if station in PBskills[oper]:
                         sub.append(PB_ass[(oper,mach)])
@@ -138,6 +142,15 @@ def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
             obj.append(subLine*weightFactors[3]/3.0)  
             
             
+    # 5th objective: prioritise machines with furthest in time last assignment 
+    LastAssignmentSum = float(sum([machineList[mach]['lastAssignment'] for mach in machines ]))
+    if LastAssignmentSum > 0 and weightFactors[4]>0:
+        obj += [machineList[mach]['lastAssignment']*PB_ass[(oper,mach)]*weightFactors[4]/float(LastAssignmentSum) for oper in PBlist for mach in machines if machineList[mach]['stationID'] in PBskills[oper]]
+       
+    # 6th objective: max the number of pb assigned
+    if weightFactors[5]>0:
+        obj += [PB_ass[(oper,mach)]*weightFactors[5]/float(len(PBlist)) for oper in PBlist for mach in machines if machineList[mach]['stationID'] in PBskills[oper]]
+       
     prob += lpSum(obj)
         
     # constraint 1: # operators assigned to a station <= 1
@@ -148,6 +161,10 @@ def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
     for operator in PBlist:
         prob += lpSum([PB_ass[(operator,machine)] for machine in machines if machineList[machine]['stationID'] in PBskills[operator]]) <= 1
             
+            
+    # write the problem data to an .lp file.
+    prob.writeLP("PBassignment.lp") 
+    
     prob.solve()
     
     if LpStatus[prob.status] != 'Optimal':
@@ -173,4 +190,3 @@ def opAss_LP(machineList, PBlist, PBskills, previousAssignment={}):
     
         
         
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-        
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