ACO execution plugin for batches instance

dream/plugins/Batches/BatchesACO.py

+from dream.plugins import plugin
+from pprint import pformat
+from copy import copy
+import json
+import time
+import random
+import operator
+import xmlrpclib
+
+from dream.simulation.Queue import Queue
+from dream.simulation.Operator import Operator
+from dream.simulation.Globals import getClassFromName
+from dream.plugins.ACO import ACO
+
+class BatchesACO(ACO):
+
+  def _calculateAntScore(self, ant):
+    """Calculate the score of this ant.
+    """
+    totalDelay=0    #set the total delay to 0
+    result, = ant['result']['result_list']  #read the result as JSON
+    #loop through the elements
+    for element in result['elementList']:
+        element_family = element.get('family', None)
+        #id the class is Job
+        if element_family == 'Job':
+            results=element['results']
+            delay = float(results.get('delay', "0"))
+            # A negative delay would mean we are ahead of schedule. This
+            # should not be considered better than being on time.
+            totalDelay += max(delay, 0)
+    return totalDelay
+
+  # creates the collated scenarios, i.e. the list 
+  # of options collated into a dictionary for ease of referencing in ManPy
+  def createCollatedScenarios(self,data):
+    collated = dict()
+    weightData=data['input'].get('ACO_weights_spreadsheet', None)
+    for i in range(1,7):
+        minValue=weightData[1][i]
+        maxValue=weightData[2][i]
+        stepValue=weightData[3][i]
+        staticValue=weightData[4][i]
+        if staticValue:
+            collated[str(i)]=[float(staticValue)]
+        else:
+            collated[str(i)]=[]
+            value=minValue
+            while 1:
+                collated[str(i)].append(round(float(value),2))
+                value+=stepValue
+                if value>maxValue:
+                    break
+    return collated    
+
+  # creates the ant scenario based on what ACO randomly selected
+  def createAntData(self,data,ant): 
+    # set scheduling rule on queues based on ant data
+    ant_data = copy(data)
+    weightFactors=[1,1,1,1,1,1]
+    for k, v in ant.items():
+        weightFactors[int(k)-1]=v
+    for node_id, node in ant_data["graph"]["node"].iteritems():
+        if node['_class']=="dream.simulation.SkilledOperatorRouter.SkilledRouter":
+            node['weightFactors']=weightFactors
+    print 'created Ant',weightFactors
+    return ant_data
+
+  def run(self, data):
+    ant_data = copy(data)
+    # below provisional values, to be updated (should the user set those?)
+    data['general']['numberOfSolutions']=1
+    data["general"]["numberOfGenerations"]=1
+    data["general"]["numberOfAntsPerGenerations"]=2
+    ACO.run(self, data)
+    return data
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