Decision Optimization

Hi everyone,
I've written a code.
When I put the parameter "TimeFrame" equal to "InFlow_6Hr " ,in line 35, I got the solution but,
when I put this parameter equal to "InFlow_4Hr" or other values, I would not get the solution and the solution is "NonType object"
I would be thankful if you guide me what should I do.
Thanks

This is the code:

""" This is the code for optimizing NajadAbad Pump Station by Pedram Jazayeri
with 1 houre periods
in a day
without electricity tarriff"""
""" v3.1.1 = delete Heights of twins and Suction revervoirs
"""

###############################################################################################
import matplotlib.pyplot as plt
from docplex.mp.model import Model
import pandas as pd
#import math
import numpy as np


###############################################################################################
#VARIABELS
Res_10000Area=(33.57-2*0.35)*(67.27-3*0.35)
Res_20000Area=(54.65-2*0.35)*(78.55-3*0.35)

Path2='D:/BMN/1401/04/vilashahr.xlsx'
Names2=['Name1', 'Name2', 'Value', 'Unit', 'Set', 'Date']
InFlowRead=pd.read_excel(Path2, sheet_name='InFlow Discharge', header=None, names=Names2)
InFlow=list(InFlowRead['Value'][0:288])
NumberOfDaysForAvailableData=int(len(InFlow)/288)

InFlow_1Hr=list(range(24*NumberOfDaysForAvailableData))
InFlow_2Hr=list(range(12*NumberOfDaysForAvailableData))
InFlow_3Hr=list(range(8*NumberOfDaysForAvailableData))
InFlow_4Hr=list(range(6*NumberOfDaysForAvailableData))
InFlow_6Hr=list(range(4*NumberOfDaysForAvailableData))


TimeFrame=InFlow_4Hr #SELECT DESIRED TIME FRAMES(1/2/3/4/5/6 HOURS)
num=int(len(TimeFrame))
print('')
print(f"You selected time period='{int(24/len(TimeFrame))} hours'. ")
print('')
period=int(24/num)

for i in range(num):
c=0
for j in range(12*period*i, 12*period*(i+1)):
c=c+InFlow[j]
TimeFrame[i]=c/(12*period)

Pump=list(range(num+1))
Pump[0]=np.nan

Net= list(range(num+1))
Net[0]=np.nan

H10= list(range(num+1))
H10[0]=3.89713526

H20= list(range(num+1))
H20[0]=3.84223247

Multipliers=[0.88, 0.63, 0.45, 0.36, 0.35, 0.44, 0.59, 0.79,\
0.98, 1.13, 1.25, 1.30, 1.32, 1.32, 1.30, 1.30,\
1.30, 1.30, 1.30, 1.28, 1.22, 1.15, 1.07, 0.99]
Sum_Multipliers=sum(Multipliers)

Daily_ConsumePredicted=34750 #cubic meters per day


###############################################################################################
#CREATE OPTIMIZATION MODEL
m = Model(name='Pump Station Optimization')
NumberOfTurnedOnPumps=list(range(num))
H10000=list(range(num))
H20000=list(range(num))
NetUsage=list(range(num))
NetConstraint=list(range(num))
H10000Constraint=list(range(num))
H20000Constraint=list(range(num))

for i in range(num):
NumberOfTurnedOnPumps[i]=m.integer_var(name='NumberOfTurnedOnPumps'+f'{i}', lb=0, ub=3)
H10000[i]=m.continuous_var(name='H10000'+f'{i}', lb=0.5 , ub=4.5)
H20000[i]=m.continuous_var(name='H20000'+f'{i}', lb=0.5 , ub=4.5)
NetUsage[i]=m.continuous_var(name='NetUsage'+f'{i}')


###############################################################################################
#CONSTRAINTS
H10000Constraint[0]=m.add_constraint((H10000[0]*Res_10000Area)==(H10[i]*Res_10000Area)+\
NumberOfTurnedOnPumps[0]*100*0.3-0.35*NetUsage[0])
H20000Constraint[0]=m.add_constraint((H20000[0]*Res_20000Area)==(H20[i]*Res_20000Area)+\
0.9*300*0.3-NumberOfTurnedOnPumps[0]*100*0.3-0.5*NetUsage[0])

if num==24:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * (Multipliers[0])))
convert_index=3.6
elif num==12:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1])/2)*2))
convert_index=3.6*2
elif num==8:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1]+Multipliers[2])/3)*3))
convert_index=3.6*3
elif num==6:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1]+Multipliers[2]+Multipliers[3])/4)*4))
convert_index=3.6*4
elif num==4:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1]+Multipliers[2]+Multipliers[3]+Multipliers[4]+Multipliers[5])/6)*6))
convert_index=3.6*6


for i in range(num-1):
H10000Constraint[i+1]=m.add_constraint((H10000[i+1]*Res_10000Area)==(H10000[i]*Res_10000Area)+\
NumberOfTurnedOnPumps[i+1]*100*convert_index-0.35*NetUsage[i+1])
H20000Constraint[i+1]=m.add_constraint((H20000[i+1]*Res_20000Area)==(H20000[i]*Res_20000Area)+\
0.9*300*convert_index-NumberOfTurnedOnPumps[i+1]*100*convert_index-0.5*NetUsage[i+1])

if num==24:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * (Multipliers[i+1])))

elif num==12:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[2*(i+1)]+Multipliers[2*(i+1)+1]) /2) *2) )

elif num==8:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[3*(i+1)]+Multipliers[3*(i+1)+1]+Multipliers[3*(i+1)+2])/3)*3))

elif num==6:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[4*(i+1)]+Multipliers[4*(i+1)+1]+Multipliers[4*(i+1)+2]+Multipliers[4*(i+1)+3])/4)*4))

elif num==4:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[6*(i+1)]+Multipliers[6*(i+1)+1]+Multipliers[6*(i+1)+2]+Multipliers[6*(i+1)+3]+Multipliers[6*(i+1)+4]+Multipliers[6*(i+1)+5])/6)*6))


###############################################################################################
#OBJECTIVE FUNCTION
if num==24:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] +NumberOfTurnedOnPumps[6] +NumberOfTurnedOnPumps[7]\
+NumberOfTurnedOnPumps[8] +NumberOfTurnedOnPumps[9] +NumberOfTurnedOnPumps[10]+NumberOfTurnedOnPumps[11]\
+NumberOfTurnedOnPumps[12]+NumberOfTurnedOnPumps[13]+NumberOfTurnedOnPumps[14]+NumberOfTurnedOnPumps[15]\
+NumberOfTurnedOnPumps[16]+NumberOfTurnedOnPumps[17]+NumberOfTurnedOnPumps[18]+NumberOfTurnedOnPumps[19]\
+NumberOfTurnedOnPumps[20]+NumberOfTurnedOnPumps[21]+NumberOfTurnedOnPumps[22]+NumberOfTurnedOnPumps[23] )

elif num==12:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] +NumberOfTurnedOnPumps[6] +NumberOfTurnedOnPumps[7]\
+NumberOfTurnedOnPumps[8] +NumberOfTurnedOnPumps[9] +NumberOfTurnedOnPumps[10]+NumberOfTurnedOnPumps[11] )

elif num==8:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] +NumberOfTurnedOnPumps[6] +NumberOfTurnedOnPumps[7] )

elif num==6:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] )

elif num==4:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3] )


###############################################################################################
#UPDATE PARAMETERS
sol=m.solve()
for i in range(num):
H10[i+1]= sol.get_value(H10000[i])
H20[i+1]= sol.get_value(H20000[i])
Pump[i+1]=sol.get_value(NumberOfTurnedOnPumps[i])
Net[i+1]= sol.get_value(NetUsage[i])
sol.display()


###############################################################################################
#PLOT
plt.plot(H20, label='Res20000')
plt.legend()
plt.show()

plt.plot(H10, label='Res10000')
plt.legend()
plt.show()

plt.plot(Multipliers, label='Hourly Multipliers', color='red')
plt.legend()
plt.show()

plt.plot(Net, label='Network Usage', color='red')
plt.legend()
plt.show()

plt.bar(list(range(1,num+1)), Pump[1:num+1], label='Number of Turnd On Pumps', color='c')
plt.legend()
plt.show()


###############################################################################################


------------------------------
Pedram Jazayeri
------------------------------

#DecisionOptimization

if sol is None, it means the engine was unable to find any solution.
If you use some settings, like a time limit for example, you should increase it and check it the engine is able to find a solution.
The model may be infeasible, then you can use the feasibility feature of cplex to diagnose which constraints/bounds are leading to this infeaisbility.

Something like

    if s:
        print("solution for a cost of %g" % m.objective_value)
        return s
    else:
        print("* model is infeasible")
        relaxer = Relaxer()
        rs = relaxer.relax(m)
        if rs:
            print("* found relaxed solution with obj=%g, relaxed=%d constraints" %
                  (relaxer.relaxed_objective_value, relaxer.number_of_relaxations))
            for ct, relaxed in relaxer.iter_relaxations():
                print("constraint: %s, relaxed by: %g" % (ct.name, relaxed))​

may help?

------------------------------
Vincent Beraudier
------------------------------

Original Message:
Sent: Tue August 02, 2022 05:12 AM
From: Pedram Jazayeri
Subject: doccplex.mp.model

Hi everyone,
I've written a code.
When I put the parameter "TimeFrame" equal to "InFlow_6Hr " ,in line 35, I got the solution but,
when I put this parameter equal to "InFlow_4Hr" or other values, I would not get the solution and the solution is "NonType object"
I would be thankful if you guide me what should I do.
Thanks

This is the code:

""" This is the code for optimizing NajadAbad Pump Station by Pedram Jazayeri
with 1 houre periods
in a day
without electricity tarriff"""
""" v3.1.1 = delete Heights of twins and Suction revervoirs
"""

###############################################################################################
import matplotlib.pyplot as plt
from docplex.mp.model import Model
import pandas as pd
#import math
import numpy as np


###############################################################################################
#VARIABELS
Res_10000Area=(33.57-2*0.35)*(67.27-3*0.35)
Res_20000Area=(54.65-2*0.35)*(78.55-3*0.35)

Path2='D:/BMN/1401/04/vilashahr.xlsx'
Names2=['Name1', 'Name2', 'Value', 'Unit', 'Set', 'Date']
InFlowRead=pd.read_excel(Path2, sheet_name='InFlow Discharge', header=None, names=Names2)
InFlow=list(InFlowRead['Value'][0:288])
NumberOfDaysForAvailableData=int(len(InFlow)/288)

InFlow_1Hr=list(range(24*NumberOfDaysForAvailableData))
InFlow_2Hr=list(range(12*NumberOfDaysForAvailableData))
InFlow_3Hr=list(range(8*NumberOfDaysForAvailableData))
InFlow_4Hr=list(range(6*NumberOfDaysForAvailableData))
InFlow_6Hr=list(range(4*NumberOfDaysForAvailableData))


TimeFrame=InFlow_4Hr #SELECT DESIRED TIME FRAMES(1/2/3/4/5/6 HOURS)
num=int(len(TimeFrame))
print('')
print(f"You selected time period='{int(24/len(TimeFrame))} hours'. ")
print('')
period=int(24/num)

for i in range(num):
c=0
for j in range(12*period*i, 12*period*(i+1)):
c=c+InFlow[j]
TimeFrame[i]=c/(12*period)

Pump=list(range(num+1))
Pump[0]=np.nan

Net= list(range(num+1))
Net[0]=np.nan

H10= list(range(num+1))
H10[0]=3.89713526

H20= list(range(num+1))
H20[0]=3.84223247

Multipliers=[0.88, 0.63, 0.45, 0.36, 0.35, 0.44, 0.59, 0.79,\
0.98, 1.13, 1.25, 1.30, 1.32, 1.32, 1.30, 1.30,\
1.30, 1.30, 1.30, 1.28, 1.22, 1.15, 1.07, 0.99]
Sum_Multipliers=sum(Multipliers)

Daily_ConsumePredicted=34750 #cubic meters per day


###############################################################################################
#CREATE OPTIMIZATION MODEL
m = Model(name='Pump Station Optimization')
NumberOfTurnedOnPumps=list(range(num))
H10000=list(range(num))
H20000=list(range(num))
NetUsage=list(range(num))
NetConstraint=list(range(num))
H10000Constraint=list(range(num))
H20000Constraint=list(range(num))

for i in range(num):
NumberOfTurnedOnPumps[i]=m.integer_var(name='NumberOfTurnedOnPumps'+f'{i}', lb=0, ub=3)
H10000[i]=m.continuous_var(name='H10000'+f'{i}', lb=0.5 , ub=4.5)
H20000[i]=m.continuous_var(name='H20000'+f'{i}', lb=0.5 , ub=4.5)
NetUsage[i]=m.continuous_var(name='NetUsage'+f'{i}')


###############################################################################################
#CONSTRAINTS
H10000Constraint[0]=m.add_constraint((H10000[0]*Res_10000Area)==(H10[i]*Res_10000Area)+\
NumberOfTurnedOnPumps[0]*100*0.3-0.35*NetUsage[0])
H20000Constraint[0]=m.add_constraint((H20000[0]*Res_20000Area)==(H20[i]*Res_20000Area)+\
0.9*300*0.3-NumberOfTurnedOnPumps[0]*100*0.3-0.5*NetUsage[0])

if num==24:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * (Multipliers[0])))
convert_index=3.6
elif num==12:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1])/2)*2))
convert_index=3.6*2
elif num==8:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1]+Multipliers[2])/3)*3))
convert_index=3.6*3
elif num==6:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1]+Multipliers[2]+Multipliers[3])/4)*4))
convert_index=3.6*4
elif num==4:
NetConstraint[0]=m.add_constraint( NetUsage[0]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[0]+Multipliers[1]+Multipliers[2]+Multipliers[3]+Multipliers[4]+Multipliers[5])/6)*6))
convert_index=3.6*6


for i in range(num-1):
H10000Constraint[i+1]=m.add_constraint((H10000[i+1]*Res_10000Area)==(H10000[i]*Res_10000Area)+\
NumberOfTurnedOnPumps[i+1]*100*convert_index-0.35*NetUsage[i+1])
H20000Constraint[i+1]=m.add_constraint((H20000[i+1]*Res_20000Area)==(H20000[i]*Res_20000Area)+\
0.9*300*convert_index-NumberOfTurnedOnPumps[i+1]*100*convert_index-0.5*NetUsage[i+1])

if num==24:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * (Multipliers[i+1])))

elif num==12:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[2*(i+1)]+Multipliers[2*(i+1)+1]) /2) *2) )

elif num==8:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[3*(i+1)]+Multipliers[3*(i+1)+1]+Multipliers[3*(i+1)+2])/3)*3))

elif num==6:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[4*(i+1)]+Multipliers[4*(i+1)+1]+Multipliers[4*(i+1)+2]+Multipliers[4*(i+1)+3])/4)*4))

elif num==4:
NetConstraint[i+1]=m.add_constraint( NetUsage[i+1]==( (Daily_ConsumePredicted/Sum_Multipliers) * ((Multipliers[6*(i+1)]+Multipliers[6*(i+1)+1]+Multipliers[6*(i+1)+2]+Multipliers[6*(i+1)+3]+Multipliers[6*(i+1)+4]+Multipliers[6*(i+1)+5])/6)*6))


###############################################################################################
#OBJECTIVE FUNCTION
if num==24:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] +NumberOfTurnedOnPumps[6] +NumberOfTurnedOnPumps[7]\
+NumberOfTurnedOnPumps[8] +NumberOfTurnedOnPumps[9] +NumberOfTurnedOnPumps[10]+NumberOfTurnedOnPumps[11]\
+NumberOfTurnedOnPumps[12]+NumberOfTurnedOnPumps[13]+NumberOfTurnedOnPumps[14]+NumberOfTurnedOnPumps[15]\
+NumberOfTurnedOnPumps[16]+NumberOfTurnedOnPumps[17]+NumberOfTurnedOnPumps[18]+NumberOfTurnedOnPumps[19]\
+NumberOfTurnedOnPumps[20]+NumberOfTurnedOnPumps[21]+NumberOfTurnedOnPumps[22]+NumberOfTurnedOnPumps[23] )

elif num==12:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] +NumberOfTurnedOnPumps[6] +NumberOfTurnedOnPumps[7]\
+NumberOfTurnedOnPumps[8] +NumberOfTurnedOnPumps[9] +NumberOfTurnedOnPumps[10]+NumberOfTurnedOnPumps[11] )

elif num==8:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] +NumberOfTurnedOnPumps[6] +NumberOfTurnedOnPumps[7] )

elif num==6:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3]\
+NumberOfTurnedOnPumps[4] +NumberOfTurnedOnPumps[5] )

elif num==4:
m.minimize( NumberOfTurnedOnPumps[0] +NumberOfTurnedOnPumps[1] +NumberOfTurnedOnPumps[2] +NumberOfTurnedOnPumps[3] )


###############################################################################################
#UPDATE PARAMETERS
sol=m.solve()
for i in range(num):
H10[i+1]= sol.get_value(H10000[i])
H20[i+1]= sol.get_value(H20000[i])
Pump[i+1]=sol.get_value(NumberOfTurnedOnPumps[i])
Net[i+1]= sol.get_value(NetUsage[i])
sol.display()


###############################################################################################
#PLOT
plt.plot(H20, label='Res20000')
plt.legend()
plt.show()

plt.plot(H10, label='Res10000')
plt.legend()
plt.show()

plt.plot(Multipliers, label='Hourly Multipliers', color='red')
plt.legend()
plt.show()

plt.plot(Net, label='Network Usage', color='red')
plt.legend()
plt.show()

plt.bar(list(range(1,num+1)), Pump[1:num+1], label='Number of Turnd On Pumps', color='c')
plt.legend()
plt.show()


###############################################################################################


------------------------------
Pedram Jazayeri
------------------------------

#DecisionOptimization