def generateDemandPlanning(input_url, PPOSQuantity=1000, PlannedWeek=1, PPOSToBeDisaggregated='PPOS1',
MinPackagingSize=10, planningHorizon=10):
"""Generate random demand from spreadsheet at input_url.
"""
# id is given as an integer and minus one
# ToDo we have to standardize data
PPOSToBeDisaggregated='PPOS'+str(PPOSToBeDisaggregated+1)
# Read data from the exported Excel file from RapidMiner and call the Import_Excel object of the KE tool to import this data in the tool
demand_data = urllib.urlopen(input_url).read()
workbook = xlrd.open_workbook(file_contents=demand_data)
worksheets = workbook.sheet_names()
worksheet_RapidMiner = worksheets[0]
A= Import_Excel()
Turnovers=A.Input_data(worksheet_RapidMiner, workbook) #Dictionary with the data from the Excel file
#Create lists with the MAs' names and the Turnovers for the first twelve weeks of 2010 retrieving this data from the dictionary
PPOS=Turnovers.get('Ppos',[])
SP=Turnovers.get('SP',[])
MA=Turnovers.get('FP Material No PGS+',[])
GlobalDemand=Turnovers.get('Global demand',[])
#Call the Distributions object and fit the data from the list in Normal distribution, so as to have info on Global demand (mean and standard deviation)
D=Distributions()
E=HandleMissingValues()
MA=E.DeleteMissingValue(MA)
t=D.Normal_distrfit(GlobalDemand)
avg=t.get('mean')
stdev=t.get('stdev')
def constrained_sum_sample_pos(n, total):
"""Return a randomly chosen list of n positive integers summing to total.
Each such list is equally likely to occur."""
dividers = sorted(random.sample(xrange(1, total), n - 1))
return [a - b for a, b in zip(dividers + [total], [0] + dividers)]
def constrained_sum_sample_nonneg(n, total):
"""Return a randomly chosen list of n nonnegative integers summing to total.
Each such list is equally likely to occur."""
return [x - 1 for x in constrained_sum_sample_pos(n, total + n)]
DemandProfile={} #Create a dictionary
week=[] # list that defines the planning horizon, i.e. 10 weeks
for i in range(int(planningHorizon)):
week.append(i+1)
for i in week:
Demand=int(abs(random.normalvariate(avg,stdev))) # Generate a random, non-negative, integer number from the Normal distribution
AllocatedPercent=0.8-(0.05*i) # Defines a number starts with 0.8 or 80% and reduced with every iteration at 0.05 or 5%
Remaining_Demand=int((1-AllocatedPercent)*Demand) # Defines the Remaining demand
a=constrained_sum_sample_nonneg(len(MA),100)
myInt=100
a=robjects.FloatVector(a)
lista = [x/myInt for x in a] # Define a list with the same length as the MA list and elements float numbers with total sum equal to 1
b=constrained_sum_sample_nonneg(len(MA),Remaining_Demand) # Define a list with the same length as the MA list and elements with total sum the Remaining demand
dicta={}
for index in range(0,len(MA)):
MinUnits=round(b[index]*(random.uniform(0,0.2)),0)
TotalUnits=b[index]
if TotalUnits<MinPackagingSize:
TotalUnits=0
if MinUnits<MinPackagingSize:
MinUnits=0
dicta.update({MA[index]:[TotalUnits,MinUnits]}) # it updates a dictionary with key the different MAs and values the remaining demand and (b[index]*lista[index])
DemandProfile.update({i:dicta}) #It updates a dictionary with key the number of each iteration (week) and value the dictionary dicta
Table=[]
i=0
for i in range(len(MA)):
Table.append([PPOS[i],SP[i],MA[i]])
i+=1
uniquePPOS=[]
for ppos in PPOS:
if not ppos in uniquePPOS and ppos!='':
uniquePPOS.append(ppos)
book=Workbook()
sheet1 = book.add_sheet('Future1', cell_overwrite_ok=True)
aggrTable=[]
for key in DemandProfile.keys():
for elem in DemandProfile[key]:
if DemandProfile[key].get(elem)[0]> 0:
MAkey=elem
totalUnits=DemandProfile[key].get(elem)[0]
minUnits=DemandProfile[key].get(elem)[1]
plannedWeek=key
aggrTable.append([MAkey,totalUnits,minUnits,plannedWeek])
else:
continue
t=1
aggrTable.sort(key=lambda x:x[1], reverse=False)
for i in sorted(aggrTable, key= lambda x:int(x[3])):
sheet1.write(0,0,'Order ID')
sheet1.write(0,1,'MA ID')
sheet1.write(0,2,'Total # Units')
sheet1.write(0,3,'Min # Units')
sheet1.write(0,4,'Planned Week')
sheet1.write(t,1, (i[0].replace('MA', '', 1)))
sheet1.write(t,2,i[1])
sheet1.write(t,3,i[2])
sheet1.write(t,4,i[3])
sheet1.write(t,0,t)
t+=1
# open json file
futureDemandProfileFile=open('futureDemandProfile.json', mode='w')
futureDemandProfile={}
t=1
for i in sorted(aggrTable, key= lambda x:int(x[3])):
dicta={'MAID':i[0],'TotalUnits':i[1],'MinUnits':i[2],'PlannedWeek':i[3]}
futureDemandProfile[t]=dicta
futureDemandProfileString=json.dumps(futureDemandProfile, indent=5)
t+=1
#write json file
futureDemandProfileFile.write(futureDemandProfileString)
###==================================================================================================###
sheet2 = book.add_sheet('PPOS', cell_overwrite_ok=True)
dictPPOS={}
dictPPOSMA={}
for ind in uniquePPOS:
indices=[i for i,j in enumerate(PPOS) if j==ind]
mas=[ma for ma in MA if (MA.index(ma) in indices)]
dictPPOSMA.update({ind: mas})
t=1
for key in dictPPOSMA.keys():
for elem in dictPPOSMA[key]:
if key==PPOSToBeDisaggregated:
c=constrained_sum_sample_nonneg(len(dictPPOSMA[key]),PPOSQuantity)
d=constrained_sum_sample_nonneg(len(dictPPOSMA[key]),100)
myInt=100
d=robjects.FloatVector(d)
listd = [x/myInt for x in d]
for i in range(0,len(dictPPOSMA[key])):
MinUnits=round(c[i]*(random.uniform(0,0.2)),0)
TotalUnits=c[i]
if TotalUnits<MinPackagingSize:
TotalUnits=0
if MinUnits<MinPackagingSize:
MinUnits=0
dictPPOS.update({dictPPOSMA[key][i]:[TotalUnits,MinUnits]})
t=1
for i in range(0,len(dictPPOS)):
sheet2.write(0,0,'Order ID')
sheet2.write(0,1,'MA ID')
sheet2.write(0,2,'Total # Units')
sheet2.write(0,3,'Min # Units')
sheet2.write(0,4,'Planned Week')
sheet2.write(t,0,t)
# XXX the MA id should not have MA prefix...
sheet2.write(t,1,dictPPOSMA[PPOSToBeDisaggregated][i].replace('MA', '', 1))
sheet2.write(t,2,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][0])
sheet2.write(t,3,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][1])
sheet2.write(t,4,PlannedWeek)
t+=1
# open json file
PPOSProfileFile=open('PPOSProfile.json', mode='w')
PPOSProfile={}
t=1
for i in range(0,len(dictPPOS)):
dictb={'MAID':dictPPOSMA[PPOSToBeDisaggregated][i],'TotalUnits':dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][0],'MinUnits':dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][1],'PlannedWeek':PlannedWeek}
PPOSProfile[t]=dictb
PPOSProfileString=json.dumps(PPOSProfile, indent=5)
t+=1
#write json file
PPOSProfileFile.write(PPOSProfileString)
import StringIO
out = StringIO.StringIO()
book.save(out)
book.save('DP.xls')
return out.getvalue()
def main(test=0,
ExcelFileName='inputData.xls',
JSONFileName='JSON_AssembleDismantle.json',
CMSDFileName='CMSD_AssemblyDismantle.xml',
workbook=None,
jsonFile=None,
cmsdFile=None):
if not workbook:
workbook = xlrd.open_workbook(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
ExcelFileName))
#Read from the given directory the Excel document with the input data
worksheets = workbook.sheet_names()
worksheet_ProcessingTimes = worksheets[
0] #Define the worksheet with the Processing times data
worksheet_MTTF = worksheets[
1] #Define the worksheet with Time-to-Failure data
worksheet_MTTR = worksheets[
2] #Define the worksheet with Time-to-Repair data
A = ImportExceldata() #Call the Python object Import_Excel
ProcessingTimes = A.Input_data(
worksheet_ProcessingTimes, workbook
) #Create the Processing Times dictionary with key the Machine 1 and values the processing time data
MTTF = A.Input_data(
worksheet_MTTF, workbook
) #Create the MTTF dictionary with key the Machine 1 and time-to-failure data
MTTR = A.Input_data(
worksheet_MTTR, workbook
) #Create the MTTR Quantity dictionary with key the Machine 1 and time-to-repair data
##Get from the above dictionaries the M1 key and define the following lists with data
ProcTime = ProcessingTimes.get('M1', [])
MTTF = MTTF.get('M1', [])
MTTR = MTTR.get('M1', [])
#Call the HandleMissingValues object and replace the missing values in the lists with the mean of the non-missing values
B = ReplaceMissingValues()
ProcTime = B.ReplaceWithMean(ProcTime)
MTTF = B.ReplaceWithMean(MTTF)
MTTR = B.ReplaceWithMean(MTTR)
C = Distributions() #Call the Distributions object
D = DistFittest() #Call the DistFittest object
ProcTime_dist = D.ks_test(ProcTime)
MTTF_dist = C.Exponential_distrfit(MTTF)
MTTR_dist = C.Exponential_distrfit(MTTR)
#======================== Output preparation: output the values prepared in the CMSD information model of this model ====================================================#
if not cmsdFile:
datafile = (
os.path.join(os.path.dirname(os.path.realpath(__file__)),
CMSDFileName)
) #It defines the name or the directory of the XML file that is manually written the CMSD information model
tree = et.parse(
datafile
) #This file will be parsed using the XML.ETREE Python library
exportCMSD = CMSDOutput()
stationId1 = 'M1'
procTime = exportCMSD.ProcessingTimes(tree, stationId1, ProcTime_dist)
TTF = exportCMSD.TTF(procTime, stationId1, MTTF_dist)
TTR = exportCMSD.TTR(TTF, stationId1, MTTR_dist)
TTR.write(
'CMSD_AssemblyDismantle_Output.xml', encoding="utf8"
) #It writes the element tree to a specified file, using the 'utf8' output encoding
#================================= Output preparation: output the updated values in the JSON file of this example =========================================================#
if not jsonFile:
jsonFile = open(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
JSONFileName), 'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
else:
data = json.load(jsonFile)
exportJSON = JSONOutput()
stationId = 'M1'
data = exportJSON.ProcessingTimes(data, stationId, ProcTime_dist)
data1 = exportJSON.TTF(data, stationId, MTTF_dist)
data2 = exportJSON.TTR(data1, stationId, MTTR_dist)
#================================ Call ManPy and run the simulation model =============================================#
#calls ManPy main script with the input
simulationOutput = ManPyMain.main(input_data=json.dumps(data2))
# if we run from test return the ManPy result
if test:
return simulationOutput
#===================== Output the JSON file ========================================#
jsonFile = open('JSON_AssembleDismantle_Output.json',
"w") #It opens the JSON file
jsonFile.write(json.dumps(
data2, indent=True)) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file
#================================ Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files =============================================#
C = ExcelOutput()
C.PrintStatisticalMeasures(ProcTime, 'ProcTime_StatResults.xls')
C.PrintStatisticalMeasures(MTTR, 'MTTR_StatResults.xls')
C.PrintStatisticalMeasures(MTTF, 'MTTF_StatResults.xls')
C.PrintDistributionFit(ProcTime, 'ProcTime_DistFitResults.xls')
C.PrintDistributionFit(MTTR, 'MTTR_DistFitResults.xls')
#================================ Call ManPy and run the simulation model =============================================#
#calls ManPy main script with the input
simulationOutput = ManPyMain.main(input_data=json.dumps(data))
# save the simulation output
jsonFile = open('ManPyOutput.json', "w") #It opens the JSON file
jsonFile.write(
simulationOutput) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file
def main(test=0,
ExcelFileName='DataSet.xlsx',
CSVFileName='ProcTimesData.csv',
simul8XMLFileName='Topology1.xml',
workbook=None,
csvFile=None,
simul8XMLFile=None):
#================================= Extract the required data from the data files ==========================================#
if csvFile:
CSVFileName = csvFile.name
filename = CSVFileName
csv = ImportCSVdata(
) #call the Import_CSV module and using its method Input_data import the data set from the CSV file to the tool
Data = csv.Input_data(filename)
Activity2_Proc = Data.get(
'Activity 2',
[]) #get from the returned Python dictionary the two data sets
Activity3_Proc = Data.get('Activity 3', [])
#Read from the given directory the Excel document with the data
if not workbook:
workbook = xlrd.open_workbook(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
ExcelFileName))
worksheets = workbook.sheet_names()
worksheet_Inter = worksheets[
0] #Define the worksheet with the Inter-arrivals time data
data = ImportExceldata()
interTimes = data.Input_data(
worksheet_Inter, workbook
) #Create the Inter-arrival times dictionary with key the Source and values the inter-arrival time data
S1 = interTimes.get('Source', [])
#Read from the given directory the Excel document with the data
worksheets = workbook.sheet_names()
worksheet_Fail = worksheets[
1] #Define the worksheet with the failures data (MTTF,MTTR)
data = ImportExceldata()
failures = data.Input_data(
worksheet_Fail, workbook
) #Create the failures dictionary with key the MTTF and MTTR data points
MTTF = failures.get('MTTF', [])
MTTR = failures.get('MTTR', [])
#======================= Fit data to probability distributions ================================#
#The Distributions and DistFittest objects are called to fit statistical distributions to the in scope data
dist = Distributions()
act2Proc = dist.Weibull_distrfit(Activity2_Proc)
act3Proc = dist.Weibull_distrfit(Activity3_Proc)
s1Times = dist.Exponential_distrfit(S1)
distFit = DistFittest()
act1MTTF = distFit.ks_test(MTTF)
act1MTTR = distFit.ks_test(MTTR)
#======================= Output preparation: output the updated values in the XML file of this example ================================#
if not simul8XMLFile:
datafile = (os.path.join(os.path.dirname(os.path.realpath(__file__)),
simul8XMLFileName)
) #It defines the name or the directory of the XML file
tree = et.parse(datafile)
else:
datafile = simul8XMLFile
tree = et.parse(datafile)
simul8 = Simul8Output() #Call the Simul8Output object
#Assign the statistical distribution calculated above in the XML file using methods of the Simul8Output object
interTimes = simul8.InterArrivalTime(tree, 'Source', s1Times)
procTimes2 = simul8.ProcTimes(interTimes, 'Activity 2', act2Proc)
procTimes3 = simul8.ProcTimes(procTimes2, 'Activity 3', act3Proc)
#Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
MTTF1 = simul8.MTBF(procTimes3, 'Activity 1', act1MTTF)
MTTR1 = simul8.MTTR(MTTF1, 'Activity 1', act1MTTR)
#Output the XML file with the processed data
output = MTTR1.write('KEtool_Topology1.xml')
if test:
output = et.parse('KEtool_Topology1.xml')
return output
D=DistFittest()
dictProc={} #Create a dictionary that holds the statistical distributions of the processing times of each station
dictProc['MA']= D.ks_test(MA_Proc)
dictProc['M1A']= D.ks_test(M1A_Proc)
dictProc['M1B']= D.ks_test(M1B_Proc)
dictProc['M2A']= D.ks_test(M2A_Proc)
dictProc['M2B']= D.ks_test(M2B_Proc)
dictProc['M3A']= D.ks_test(M3A_Proc)
dictProc['M3B']= D.ks_test(M3B_Proc)
dictProc['MM']= D.ks_test(MM_Proc)
dictProc['PrA']= D.ks_test(PrA_Proc)
dictProc['PrB']= D.ks_test(PrB_Proc)
dictProc['PaA']= D.ks_test(PaA_Proc)
dictProc['PaB']= D.ks_test(PaB_Proc)
#Call the Distributions object and fit (using the Maximum Likelihood Estimation) the lists with the scrap quantity into a discrete statistical distribution, i.e. Geometric distribution
D=Distributions()
dictScrap={} #Create a dictionary that holds the Geometric, which is a discrete statistical distribution of the processing times of each station
dictScrap['MA']= D.Geometric_distrfit(MA_Scrap)
dictScrap['M1A']= D.Geometric_distrfit(M1A_Scrap)
dictScrap['M1B']= D.Geometric_distrfit(M1B_Scrap)
dictScrap['M2A']= D.Geometric_distrfit(M2A_Scrap)
dictScrap['M2B']= D.Geometric_distrfit(M2B_Scrap)
dictScrap['M3A']= D.Geometric_distrfit(M3A_Scrap)
dictScrap['M3B']= D.Geometric_distrfit(M3B_Scrap)
dictScrap['MM']= D.Geometric_distrfit(MM_Scrap)
dictScrap['PrA']= D.Geometric_distrfit(PrA_Scrap)
dictScrap['PrB']= D.Geometric_distrfit(PrB_Scrap)
dictScrap['PaA']= D.Geometric_distrfit(PaA_Scrap)
dictScrap['PaB']= D.Geometric_distrfit(PaB_Scrap)
#Call the CMSDOutput object giving as attributes the dictionaries with the processing times distributions and the scrap quantities distributions
def main(test=0,
simul8XMLFileName='ParallelStations.xml',
DBFilePath='C:\Users\Panos\Documents\KE tool_documentation',
file_path=None,
simul8XMLFile=None):
if not file_path:
cnxn = ConnectionData(seekName='ServerData',
file_path=DBFilePath,
implicitExt='txt',
number_of_cursors=3)
cursors = cnxn.getCursors()
#Database queries used to extract the required data, in this example the processing times are given subtracting the TIME IN data point from the TIME OUT data point
a = cursors[0].execute("""
select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
from production_status
""")
MILL1 = [] #Initialization of MILL1 list
MILL2 = [] #Initialization of MILL2 list
for j in range(a.rowcount):
#get the next line
ind1 = a.fetchone()
if ind1.stat_code == 'MILL1':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL1.append(procTime)
elif ind1.stat_code == 'MILL2':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL2.append(procTime)
else:
continue
#The BasicTransformations object is called to conduct some data transformations
transform = Transformations()
procTime_MILL1 = []
for elem in MILL1:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL1.append(dt[0])
procTime_MILL2 = []
for elem in MILL2:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL2.append(dt[0])
#Database queries used again to extract the MTTF and MTTR data points
b = cursors[1].execute("""
select stat_code, MTTF_hour
from failures
""")
c = cursors[2].execute("""
select stat_code, MTTR_hour
from repairs
""")
MTTF_MILL1 = [
] #Initialization of the list that will contain the MTTF data points for MILL1
MTTF_MILL2 = [
] #Initialization of the list that will contain the MTTF data points for MILL2
for j in range(b.rowcount):
#get the next line
ind2 = b.fetchone()
if ind2.stat_code == 'MILL1':
MTTF_MILL1.append(ind2.MTTF_hour)
elif ind2.stat_code == 'MILL2':
MTTF_MILL2.append(ind2.MTTF_hour)
else:
continue
MTTR_MILL1 = [
] #Initialization of the list that will contain the MTTR data points for MILL1
MTTR_MILL2 = [
] #Initialization of the list that will contain the MTTR data points for MILL1
for j in range(c.rowcount):
#get the next line
ind3 = c.fetchone()
if ind3.stat_code == 'MILL1':
MTTR_MILL1.append(ind3.MTTR_hour)
elif ind3.stat_code == 'MILL2':
MTTR_MILL2.append(ind3.MTTR_hour)
else:
continue
#======================= Fit data to statistical distributions ================================#
#The Distributions object is called to fit statistical distributions to the in scope data
dist_proctime = Distributions()
distProcTime_MILL1 = dist_proctime.Lognormal_distrfit(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.Weibull_distrfit(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Exponential_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Exponential_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Normal_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Normal_distrfit(MTTR_MILL2)
#======================= Output preparation: output the updated values in the XML file of this example ================================#
if not simul8XMLFile:
datafile = (os.path.join(os.path.dirname(os.path.realpath(__file__)),
simul8XMLFileName)
) #It defines the name or the directory of the XML file
tree = et.parse(datafile)
else:
datafile = simul8XMLFile
tree = et.parse(datafile)
simul8 = Simul8Output() #Call the Simul8Output object
#Assign the statistical distribution found above in the XML file using methods of the Simul8Output object
procTimes1 = simul8.ProcTimes(tree, 'MILL1', distProcTime_MILL1)
procTimes2 = simul8.ProcTimes(procTimes1, 'MILL2', distProcTime_MILL2)
#Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
MTTF1 = simul8.MTBF(procTimes2, 'MILL1', distMTTF_MILL1)
MTTR1 = simul8.MTTR(MTTF1, 'MILL1', distMTTR_MILL1)
MTTF2 = simul8.MTBF(MTTR1, 'MILL2', distMTTF_MILL2)
MTTR2 = simul8.MTTR(MTTF2, 'MILL2', distMTTR_MILL2)
#Output the XML file with the processed data
output = MTTR2.write('KEtool_ParallelStations.xml')
if test:
output = et.parse('KEtool_ParallelStations.xml')
return output
def main(test=0,
JSONFileName='JSON_example.json',
CMSDFileName='CMSD_ParallelStations.xml',
DBFilePath='C:\Users\Panos\Documents\KE tool_documentation',
file_path=None,
jsonFile=None,
cmsdFile=None):
if not file_path:
cnxn = ConnectionData(seekName='ServerData',
file_path=DBFilePath,
implicitExt='txt',
number_of_cursors=3)
cursors = cnxn.getCursors()
a = cursors[0].execute("""
select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
from production_status
""")
MILL1 = []
MILL2 = []
for j in range(a.rowcount):
#get the next line
ind1 = a.fetchone()
if ind1.stat_code == 'MILL1':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL1.append(procTime)
elif ind1.stat_code == 'MILL2':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL2.append(procTime)
else:
continue
transform = Transformations()
procTime_MILL1 = []
for elem in MILL1:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL1.append(dt[0])
procTime_MILL2 = []
for elem in MILL2:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL2.append(dt[0])
b = cursors[1].execute("""
select stat_code, MTTF_hour
from failures
""")
c = cursors[2].execute("""
select stat_code, MTTR_hour
from repairs
""")
MTTF_MILL1 = []
MTTF_MILL2 = []
for j in range(b.rowcount):
#get the next line
ind2 = b.fetchone()
if ind2.stat_code == 'MILL1':
MTTF_MILL1.append(ind2.MTTF_hour)
elif ind2.stat_code == 'MILL2':
MTTF_MILL2.append(ind2.MTTF_hour)
else:
continue
MTTR_MILL1 = []
MTTR_MILL2 = []
for j in range(c.rowcount):
#get the next line
ind3 = c.fetchone()
if ind3.stat_code == 'MILL1':
MTTR_MILL1.append(ind3.MTTR_hour)
elif ind3.stat_code == 'MILL2':
MTTR_MILL2.append(ind3.MTTR_hour)
else:
continue
#======================= Fit data to statistical distributions ================================#
dist_proctime = DistFittest()
distProcTime_MILL1 = dist_proctime.ks_test(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.ks_test(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Weibull_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Weibull_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Poisson_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Poisson_distrfit(MTTR_MILL2)
#======================== Output preparation: output the values prepared in the CMSD information model of this model ====================================================#
if not cmsdFile:
datafile = (
os.path.join(os.path.dirname(os.path.realpath(__file__)),
CMSDFileName)
) #It defines the name or the directory of the XML file that is manually written the CMSD information model
tree = et.parse(
datafile
) #This file will be parsed using the XML.ETREE Python library
exportCMSD = CMSDOutput()
stationId1 = 'M1'
stationId2 = 'M2'
procTime1 = exportCMSD.ProcessingTimes(tree, stationId1,
distProcTime_MILL1)
procTime2 = exportCMSD.ProcessingTimes(procTime1, stationId2,
distProcTime_MILL2)
TTF1 = exportCMSD.TTF(procTime2, stationId1, distMTTF_MILL1)
TTR1 = exportCMSD.TTR(TTF1, stationId1, distMTTR_MILL1)
TTF2 = exportCMSD.TTF(TTR1, stationId2, distMTTF_MILL2)
TTR2 = exportCMSD.TTR(TTF2, stationId2, distMTTR_MILL2)
TTR2.write(
'CMSD_ParallelStations_Output.xml', encoding="utf8"
) #It writes the element tree to a specified file, using the 'utf8' output encoding
#======================= Output preparation: output the updated values in the JSON file of this example ================================#
if not jsonFile:
jsonFile = open(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
JSONFileName), 'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
else:
data = json.load(jsonFile)
exportJSON = JSONOutput()
stationId1 = 'M1'
stationId2 = 'M2'
data1 = exportJSON.ProcessingTimes(data, stationId1, distProcTime_MILL1)
data2 = exportJSON.ProcessingTimes(data1, stationId2, distProcTime_MILL2)
data3 = exportJSON.TTF(data2, stationId1, distMTTF_MILL1)
data4 = exportJSON.TTR(data3, stationId1, distMTTR_MILL1)
data5 = exportJSON.TTF(data4, stationId2, distMTTF_MILL2)
data6 = exportJSON.TTR(data5, stationId2, distMTTR_MILL2)
# if we run from test return the data6
if test:
return data6
jsonFile = open('JSON_ParallelStations_Output.json',
"w") #It opens the JSON file
jsonFile.write(json.dumps(
data6, indent=True)) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file
#=================== Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files ==========================#
export = ExcelOutput()
export.PrintStatisticalMeasures(procTime_MILL1,
'procTimeMILL1_StatResults.xls')
export.PrintStatisticalMeasures(procTime_MILL2,
'procTimeMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL1, 'MTTFMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL2, 'MTTFMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL1, 'MTTRMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL2, 'MTTRMILL2_StatResults.xls')
export.PrintDistributionFit(procTime_MILL1,
'procTimeMILL1_DistFitResults.xls')
export.PrintDistributionFit(procTime_MILL2,
'procTimeMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL1, 'MTTFMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL2, 'MTTFMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL1, 'MTTRMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL2, 'MTTRMILL2_DistFitResults.xls')
def main(test=0,
ExcelFileName='inputData.xls',
JSONFileName='JSON_ParallelStations.json',
workbook=None,
jsonFile=None):
#Read from the given directory the Excel document with the input data
if not workbook:
workbook = xlrd.open_workbook(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
ExcelFileName))
worksheets = workbook.sheet_names()
worksheet_ProcessingTimes = worksheets[
0] #Define the worksheet with the Processing times data
inputData = ImportExceldata() #Call the Python object Import_Excel
ProcessingTimes = inputData.Input_data(
worksheet_ProcessingTimes, workbook
) #Create the Processing Times dictionary with key Machines 1,2 and values the processing time data
##Get from the above dictionaries the M1 key and define the following lists with data
M1_ProcTime = ProcessingTimes.get('M1', [])
M2_ProcTime = ProcessingTimes.get('M2', [])
#Call the HandleMissingValues object and replace the missing values in the lists with the mean of the non-missing values
misValues = ReplaceMissingValues()
M1_ProcTime = misValues.ReplaceWithMean(M1_ProcTime)
M2_ProcTime = misValues.ReplaceWithMean(M2_ProcTime)
MLE = Distributions(
) #Call the Distributions object (Maximum Likelihood Estimation - MLE)
KS = DistFittest(
) #Call the DistFittest object (Kolmoghorov-Smirnov test)
M1ProcTime_dist = KS.ks_test(M1_ProcTime)
M2ProcTime_dist = MLE.Normal_distrfit(M2_ProcTime)
#======================= Output preparation: output the updated values in the JSON file of this example ================================#
if not jsonFile:
jsonFile = open(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
JSONFileName), 'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
else:
data = json.load(jsonFile)
exportJSON = JSONOutput()
stationId1 = 'St1'
stationId2 = 'St2'
data1 = exportJSON.ProcessingTimes(data, stationId1, M1ProcTime_dist)
data2 = exportJSON.ProcessingTimes(data1, stationId2, M2ProcTime_dist)
#================================ Call ManPy and run the simulation model =============================================#
#calls ManPy main script with the input
simulationOutput = ManPyMain.main(input_data=json.dumps(data2))
# if we run from test return the ManPy result
if test:
return simulationOutput
#=================== Ouput the JSON file ==========================#
jsonFile = open('JSON_ParallelStations_Output.json',
"w") #It opens the JSON file
jsonFile.write(json.dumps(
data2, indent=True)) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file
#=================== Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files ==========================#
export = ExcelOutput()
export.PrintStatisticalMeasures(M1_ProcTime, 'M1_ProcTime_StatResults.xls')
export.PrintStatisticalMeasures(M2_ProcTime, 'M2_ProcTime_StatResults.xls')
export.PrintDistributionFit(M1_ProcTime, 'M1_ProcTime_DistFitResults.xls')
export.PrintDistributionFit(M2_ProcTime, 'M2_ProcTime_DistFitResults.xls')
# save the simulation output
jsonFile = open('ManPyOutput.json', "w") #It opens the JSON file
jsonFile.write(
simulationOutput) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file
def main(test=0,
ExcelFileName1='InterarrivalsData.xls',
ExcelFileName2='ProcData.xls',
simul8XMLFileName='SingleServer.xml',
workbook1=None,
workbook2=None,
simul8XMLFile=None):
#Read from the given directory the Excel document with the processing times data
if not workbook2:
workbook2 = xlrd.open_workbook(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
ExcelFileName2))
worksheets = workbook2.sheet_names()
worksheet_Proc = worksheets[
0] #Define the worksheet with the Processing time data
importData = ImportExceldata() #Call the Python object Import_Excel
procTimes = importData.Input_data(
worksheet_Proc, workbook2
) #Create the Processing times dictionary with key the M1 and values the processing time data
#Get from the above dictionaries the M1 key and the Source key and define the following lists with data
M1 = procTimes.get('M1', [])
distFitting = DistFittest() #Call the DistFittest object
M1 = distFitting.ks_test(M1)
#Read from the given directory the Excel document with the inter-arrivals data
if not workbook1:
workbook1 = xlrd.open_workbook(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
ExcelFileName1))
worksheets = workbook1.sheet_names()
worksheet_Inter = worksheets[
0] #Define the worksheet with the Inter-arrivals time data
data = ImportExceldata()
interTimes = data.Input_data(
worksheet_Inter, workbook1
) #Create the Inter-arrival times dictionary with key the Source and values the inter-arrival time data
S1 = interTimes.get('Source', [])
distMLE = Distributions() #Call the Distributions object
S1 = distMLE.Exponential_distrfit(S1)
if not simul8XMLFile:
datafile = (os.path.join(os.path.dirname(os.path.realpath(__file__)),
simul8XMLFileName)
) #It defines the name or the directory of the XML file
tree = et.parse(datafile)
else:
datafile = simul8XMLFile
tree = et.parse(datafile)
simul8 = Simul8Output() #Call the Simul8Output object
title = 'KEtool_SingleServer'
interTimes = simul8.InterArrivalTime(tree, 'Source', S1)
procTimes = simul8.ProcTimes(interTimes, 'Activity 1', M1)
title = simul8.Title(procTimes, title)
#Output the XML file with the processed data
output = title.write('KEtool_SingleServer.xml')
if test:
output = et.parse('KEtool_SingleServer.xml')
return output
def main(test=0,
CSVFileName1='InterArrivalData.csv',
CSVFileName2='DataSet.csv',
JSONFileName='JSON_ConveyerLine.json',
jsonFile=None,
csvFile1=None,
csvFile2=None):
if csvFile2:
CSVFileName2 = csvFile2.name
if csvFile1:
CSVFileName1 = csvFile1.name
CSV = ImportCSVdata(
) #call the Import_CSV module and using its method Input_data import the data set from the CSV file to the tool
procData = CSV.Input_data(CSVFileName2)
sourceData = CSV.Input_data(CSVFileName1)
M1 = procData.get(
'M1', []) #get from the returned Python dictionary the data sets
M2 = procData.get('M2', [])
S1 = sourceData.get('S1', [])
################### Processing of the data sets calling the following objects ###################################
#Replace missing values calling the corresponding object
missingValues = ReplaceMissingValues()
M1 = missingValues.DeleteMissingValue(M1)
M2 = missingValues.DeleteMissingValue(M2)
S1 = missingValues.ReplaceWithMean(S1)
#Detect outliers calling the DetectOutliers object
outliers = DetectOutliers()
M1 = outliers.DeleteExtremeOutliers(M1)
M2 = outliers.DeleteExtremeOutliers(M2)
S1 = outliers.DeleteOutliers(S1)
#Conduct distribution fitting calling the Distributions object and DistFittest object
MLE = Distributions()
KStest = DistFittest()
M1 = KStest.ks_test(M1)
M2 = KStest.ks_test(M2)
S1 = MLE.Exponential_distrfit(S1)
#================================= Output preparation: output the updated values in the JSON file of this example =========================================================#
if not jsonFile:
jsonFile = open(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
JSONFileName), 'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
else:
data = json.load(jsonFile)
exportJSON = JSONOutput()
stationId1 = 'M1'
stationId2 = 'M2'
stationId3 = 'S1'
data = exportJSON.ProcessingTimes(data, stationId1, M1)
data1 = exportJSON.ProcessingTimes(data, stationId2, M2)
data2 = exportJSON.InterarrivalTime(data1, stationId3, S1)
# if we run from test return the data2
if test:
return data2
jsonFile = open('JSON_ConveyerLine_Output.json',
"w") #It opens the JSON file
jsonFile.write(json.dumps(
data2, indent=True)) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file