added antOptimiser and LineGenerationJSONAntOptimise. The first is an...

added antOptimiser and LineGenerationJSONAntOptimise. The first is an optimisation algorithm based on scheduling rules. Second is the main ManPy script that the AntOptimiser calls when he needs to evaluate

dream/simulation/AntOptimiser.py

+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+'''
+Created on 22 Nov 2013
+
+@author: Dipo, George
+'''
+'''
+Optimization based on scenario analysis 
+For now it is only mixing different scheduling rules of the Queues
+'''
+
+import random
+import json
+import LineGenerationJSONAntOptimiser
+import time
+
+def ranking(candidates,elg): #this function is used for ranking and selection of the best ant
+    pop = [] #this list is used separately to extract ant scores for ranking them
+    for ch in candidates: #it loops through the list of ants and append their scores to a list
+        pop.append(ch['score']) #it append the scores to the list pop
+    pop.sort() #pop = list(set(pop)) #the scores are sorted in ascending order
+    del pop[elg:] #from the sorted list, a certain number specified as elg are only retained - others are deleted
+    fittest = [] #Now, another list is created for the best ranked ants
+    for chrom in candidates: #the retained scored are matched with Ants
+        if chrom['score'] in pop:#if an ants score was retained, it becomes retained too
+            fittest.append(chrom)
+            pop.remove(chrom['score'])#as the associated ants are appended the scores are immediately deleted to avoid confusion
+    return fittest #returns the fittest Ants of the generation
+
+#method that takes an ant and creates a json with the scheduling rules in order to send it to ManPy main script
+def createAntJSON(ant):
+    antJSON={}
+    antJSON["_class"] = "Dream.Simulation" 
+    antJSON["nodes"] = {}
+    for record in ant:
+        stationId=str(record)
+        schedulingRule=ant.get(stationId, "FIFO")
+        antJSON["nodes"][stationId] = {}
+        antJSON["nodes"][stationId]["_class"]="Dream.QueueJobShop"
+        antJSON["nodes"][stationId]["schedulingRule"]=schedulingRule
+    return json.dumps(antJSON, indent=True)
+
+def calculateAntTotalDelay(ant):
+    totalDelay=0    #set the total delay to 0
+    jsonData=json.loads(ant['resultJSON'])  #read the result as JSON
+    elementList = jsonData['elementList']   #find the route of JSON
+    #loop through the elements
+    for element in elementList:
+        elementClass=element['_class']  #get the class
+        #id the class is Job
+        if elementClass=='Dream.Job':
+            results=element['results']  
+            delay=float(results.get('delay', "0")) 
+            totalDelay+=delay
+    return totalDelay
+
+'''
+Below are initial lists of scheduling rules to be considered for each of the queues/machine, these could be entered at GUI level
+
+I believe the ability to attribute each of these lists to a queue object is the only requirements at GUI level. Everything henceforth is a ManPy and Optimization business.
+'''
+#M1Options = ['EDD','WINQ','LPT','SPT','SRR','ERD','PCO','MS'] #initial list of scheduling rules that are to be randomly used for each of the machines
+#M2Options = ['EDD','WINQ','LPT','SPT','SRR','ERD','PCO','MS']
+#M3Options = ['EDD','WINQ','LPT','SPT','SRR','ERD','PCO','MS']
+#M4Options = ['EDD','WINQ','LPT','SPT','SRR','ERD','PCO','MS']
+#M5Options = ['EDD','WINQ','LPT','SPT','SRR','ERD','PCO','MS']
+
+M1Options = ['EDD','NextStage','EOD','Priority','RPC','MinSlack','NumStages'] #initial list of scheduling rules that are to be randomly used for each of the machines
+M2Options = ['EDD','NextStage','EOD','Priority','RPC','MinSlack','NumStages'] 
+M3Options = ['EDD','NextStage','EOD','Priority','RPC','MinSlack','NumStages'] 
+M4Options = ['EDD','NextStage','EOD','Priority','RPC','MinSlack','NumStages'] 
+M5Options = ['EDD','NextStage','EOD','Priority','RPC','MinSlack','NumStages'] 
+
+#Optimization takes over from here and it calls ManPy simulation at intervals using the sim function 
+def main():
+    start=time.time()                               # start counting execution time 
+    modelJSONFile=open("C:\Users\George\dream\dream\simulation\JSONInputs\Topology20.JSON", "r")     #the JSON of the model. To be sent from GUI
+    modelJSONData=modelJSONFile.read()   
+    modelJSON=json.loads(modelJSONData)
+    modelJSON=json.dumps(modelJSON, indent=True)
+    collated = {'Q1':M2Options,'Q2':M3Options,'Q3':M4Options,'Q4':M4Options,'Q5':M5Options} #the list of options collated into a dictionary for ease of referencing in ManPy
+    ants = [] #list of ants for keeping track of their performance
+    
+    for i in range(6): #Number of times new ants are to be created, i.e. number of generations (a generation can have more than 1 ant)
+        for j in range(8): #number of ants created per generation
+            ant = {} #an ant dictionary to contain rule to queue assignment information 
+            for k in collated.keys(): #for each of the machines, rules are randomly picked from the options list 
+                    ant[str(k)] = random.choice(collated[str(k)])
+            ants.append(ant) #the current ant to be simulated (evaluated) is added to the ants list
+            antJSON=createAntJSON(ant)
+            ant['resultJSON'] = LineGenerationJSONAntOptimiser.main(modelJSON,antJSON)         
+            ant['score'] = calculateAntTotalDelay(ant) 
+        ants = ranking(ants,4) #the ants in this generation are ranked based on their scores and the best 4 are selected
+
+        for l in ants: #update the options list to ensure that good performing queue-rule combinations have increased representation and good chance of being selected in the next generation
+            for m in collated.keys():#e.g. if using EDD gave good performance for  Queue 1, then another 'EDD' is added to M1Options so there is a higher chance that it is selected by the next ants.
+                collated[m].append(l[m])
+        #print collated#to verify that the list of options was updated accordingly
+    print 'Best 3 results'
+    print '================='
+    print ants[0]['score']
+    print '================='
+    print ants[1]['score']
+    print '================='
+    print ants[2]['score']
+    print "execution time=", str(time.time()-start)
+
+if __name__ == '__main__':
+    main()
+