from dream.simulation.imports import BatchScrapMachine, NonStarvingEntry, Exit, Part
from dream.simulation.Globals import runSimulation
#define the objects of the model
NS=NonStarvingEntry('NS1','Entry',entityData={'_class':'Dream.Batch','numberOfUnits':100})
M=BatchScrapMachine('M1','Machine', processingTime={'Fixed':{'mean':0.02}})
E=Exit('E1','Exit')
#define predecessors and successors for the objects
NS.defineRouting(successorList=[M])
M.defineRouting(predecessorList=[NS],successorList=[E])
E.defineRouting(predecessorList=[M])
def main(test=0):
# add all the objects in a list
objectList=[NS,M,E]
# set the length of the experiment
maxSimTime=10
# call the runSimulation giving the objects and the length of the experiment
runSimulation(objectList, maxSimTime)
# calculate metrics
working_ratio = (M.totalWorkingTime/maxSimTime)*100
# return results for the test
if test:
return {"batches": E.numOfExits,
"working_ratio": working_ratio}
#print the results
print "the system produced", E.numOfExits, "batches"
from dream.simulation.imports import Machine, Source, Exit, Part, Queue, NonStarvingEntry
from dream.simulation.Globals import runSimulation
#define the objects of the model
NS = NonStarvingEntry('NS1', 'Entry', entityData={'_class': 'Dream.Part'})
M1 = Machine('M1', 'Machine1', processingTime={'Exp': {'mean': 1}})
Q2 = Queue('Q2', 'Queue2')
M2 = Machine('M2', 'Machine2', processingTime={'Exp': {'mean': 3}})
Q3 = Queue('Q3', 'Queue3')
M3 = Machine('M3', 'Machine3', processingTime={'Exp': {'mean': 5}})
E = Exit('E1', 'Exit')
#define predecessors and successors for the objects
NS.defineRouting(successorList=[M1])
M1.defineRouting(predecessorList=[NS], successorList=[Q2])
Q2.defineRouting(predecessorList=[M1], successorList=[M2])
M2.defineRouting(predecessorList=[Q2], successorList=[Q3])
Q3.defineRouting(predecessorList=[M2], successorList=[M3])
M3.defineRouting(predecessorList=[Q3], successorList=[E])
E.defineRouting(predecessorList=[M3])
def main(test=0):
# add all the objects in a list
objectList = [NS, M1, M2, M3, Q2, Q3, E]
# set the length of the experiment
maxSimTime = 480
solutionList = []
for i in range(1, 10):
for obj in [M1, M2, M3, B123]:
G.totalWIP += len(obj.getActiveObjectQueue())
# at the end of the simulation append to the list that keeps for all replications
if G.env.now == G.maxSimTime - 1:
G.AverageWIP.append(G.totalWIP / float(G.maxSimTime))
# returns a number from the uniform distribution (0,1)
def createRandomNumber():
return Rnd.uniform(0, 1)
#define the objects of the model
NS1 = NonStarvingEntry('NS1',
'Entry1',
entityData={
'_class': 'Dream.Part',
'status': 'Good'
})
NS2 = NonStarvingEntry('NS2',
'Entry2',
entityData={
'_class': 'Dream.Part',
'status': 'Good'
})
M1 = OpMachine('M1', 'Machine1', processingTime={'Fixed': {'mean': 0.1}})
M2 = OpMachine('M2', 'Machine2', processingTime={'Fixed': {'mean': 0.1}})
M3 = OpMachine('M3', 'Machine3', processingTime={'Fixed': {'mean': 0.1}})
B123 = OpQueue('B123', 'Queue', capacity=capacity, gatherWipStat=True)
E = OpExit('E1', 'Exit')
Controller = EventGenerator('EV',
'Controller',
from dream.simulation.imports import BatchScrapMachine, NonStarvingEntry, Exit, Part
from dream.simulation.Globals import runSimulation
#define the objects of the model
NS = NonStarvingEntry('NS1',
'Entry',
entityData={
'_class': 'Dream.Batch',
'numberOfUnits': 100
})
M = BatchScrapMachine('M1',
'Machine',
processingTime={'Fixed': {
'mean': 0.02
}})
E = Exit('E1', 'Exit')
#define predecessors and successors for the objects
NS.defineRouting(successorList=[M])
M.defineRouting(predecessorList=[NS], successorList=[E])
E.defineRouting(predecessorList=[M])
def main(test=0):
# add all the objects in a list
objectList = [NS, M, E]
# set the length of the experiment
maxSimTime = 10
# call the runSimulation giving the objects and the length of the experiment
runSimulation(objectList, maxSimTime)
# count the total WIP for the machines and the Queue
for obj in [M1,M2,M3,B123]:
G.totalWIP+=len(obj.getActiveObjectQueue())
# at the end of the simulation append to the list that keeps for all replications
if G.env.now==G.maxSimTime-1:
G.AverageWIP.append(G.totalWIP/float(G.maxSimTime))
# returns a number from the uniform distribution (0,1)
def createRandomNumber():
return Rnd.uniform(0,1)
#define the objects of the model
NS1=NonStarvingEntry('NS1','Entry1',entityData={'_class':'Dream.Part','status':'Good'})
NS2=NonStarvingEntry('NS2','Entry2',entityData={'_class':'Dream.Part','status':'Good'})
M1=OpMachine('M1','Machine1', processingTime={'Fixed':{'mean':0.1}})
M2=OpMachine('M2','Machine2', processingTime={'Fixed':{'mean':0.1}})
M3=OpMachine('M3','Machine3', processingTime={'Fixed':{'mean':0.1}})
B123=OpQueue('B123','Queue', capacity=capacity,gatherWipStat=True)
E=OpExit('E1','Exit')
Controller=EventGenerator('EV','Controller',start=0,interval=1,method=controllerMethod)
#define predecessors and successors for the objects
NS1.defineRouting(successorList=[M1])
NS2.defineRouting(successorList=[M2])
M1.defineRouting(predecessorList=[NS1],successorList=[B123])
M2.defineRouting(predecessorList=[NS2],successorList=[B123])
B123.defineRouting(predecessorList=[M1,M2],successorList=[M3])
M3.defineRouting(predecessorList=[B123],successorList=[E])
