Knowledge Extraction tool new pilot case

dream/KnowledgeExtraction/PilotCases/InputData_DemandPlanning.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 7 May 2014
+
+@author: Panos
+'''
+
+from DistributionFitting import Distributions
+from ImportExceldata import Import_Excel
+from ReplaceMissingValues import HandleMissingValues
+import rpy2.robjects as robjects
+from xlwt import Workbook
+import json
+import xlrd
+import random
+
+#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
+workbook = xlrd.open_workbook(r'Input8PPOS.xlsx','r')     
+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
+MinPackagingSize=10
+week=[1,2,3,4,5,6,7,8,9,10] # list that defines the planning horizon, i.e. 10 weeks
+
+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('Future Demand Profile', cell_overwrite_ok=True)
+# r=0
+# for key in DemandProfile.keys():
+#     t=1
+#     for elem in DemandProfile[key]:
+#         sheet1.write(2+t,0,elem)
+#         sheet1.write(t+2,key+r,DemandProfile[key].get(elem)[0])
+#         sheet1.write(t+2,key+r+1,DemandProfile[key].get(elem)[1])
+#         book.save('Output8PPOS.xls')
+#         t+=1
+#     r+=1
+###======================================================================================================###
+book=Workbook()
+sheet1 = book.add_sheet('New Output', 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])
+    sheet1.write(t,2,i[1])
+    sheet1.write(t,3,i[2])
+    sheet1.write(t,4,i[3])
+    sheet1.write(t,0,t)    
+    book.save('NewOutput8PPOS.xls')
+    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)
+        
+###==================================================================================================### 
+book=Workbook()
+sheet1 = book.add_sheet('PPOS Profile', cell_overwrite_ok=True)
+
+PPOSToBeDisaggregated='PPOS4'
+PPOSQuantity=1000
+PlannedWeek=2
+
+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)):
+    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,0,t)
+    sheet1.write(t,1,dictPPOSMA[PPOSToBeDisaggregated][i])
+    sheet1.write(t,2,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][0])
+    sheet1.write(t,3,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][1])
+    sheet1.write(t,4,PlannedWeek)
+    book.save('PPOSOutput8PPOS.xls')
+    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)
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