Hi

 

again

 

 

At https://www.ibm.com/developerworks/community/forums/html/topic?id=a8580237-639a-411b-b1df-f390a24da06c&ps=25

 

we saw small examples where data is in tuple but what about pandas data frame ?

 

Here we are:

 

https://www.linkedin.com/pulse/what-optimization-how-can-help-you-do-more-less-zoo-buses-fleischer/

The Python docplex version is

from docplex.mp.model import Model

mdl = Model(name='buses')
nbbus40 = mdl.integer_var(name='nbBus40')
nbbus30 = mdl.integer_var(name='nbBus30')
mdl.add_constraint(nbbus40*40 + nbbus30*30 >= 300, 'kids')
mdl.minimize(nbbus40*500 + nbbus30*400)

mdl.solve()

mdl.export("c:\\buses.lp")

for v in mdl.iter_integer_vars():
    print(v," = ",v.solution_value)

Now let s try to be more generic and get the data from pandas dataframe.

We may rely on:

• a dictionnary integer_var_dict

from docplex.mp.model import Model
from pandas import *

# Data

dfBuses = pandas.DataFrame({'size': [40,30], 'cost': [500,400]},
                      index = ['bus1', 'bus2'], columns=['size','cost'])

print(dfBuses)

nbKids=300

for b in dfBuses.itertuples():
    print("buses with ",b.size," seats cost ",b.cost)

mdl = Model(name='buses')

#decision variables
mdl.nbBus=mdl.integer_var_dict(dfBuses.itertuples(),name="nbBus")

# Constraint
mdl.add_constraint(sum(mdl.nbBus[b]*b.size for b in dfBuses.itertuples()) >= nbKids, 'kids')

# Objective
mdl.minimize(sum(mdl.nbBus[b]*b.cost for b in dfBuses.itertuples()))

mdl.solve()

# Dislay solution
for b in dfBuses.itertuples():
    print(mdl.nbBus[b].solution_value," buses with ",b.size," seats");

 

• variables integer_var

from docplex.mp.model import Model
from pandas import *

# Data

dfBuses = pandas.DataFrame({'size': [40,30], 'cost': [500,400]},
                      index = ['bus1', 'bus2'], columns=['size','cost'])

print(dfBuses)

nbKids=300

for b in dfBuses.itertuples():
    print("buses with ",b.size," seats cost ",b.cost)

mdl = Model(name='buses')

#decision variables
mdl.nbBus = {b: mdl.integer_var(name="nbBus"+str(b.size)) for b in dfBuses.itertuples()}

# Constraint
mdl.add_constraint(sum(mdl.nbBus[b]*b.size for b in dfBuses.itertuples()) >= nbKids, 'kids')

# Objective
mdl.minimize(sum(mdl.nbBus[b]*b.cost for b in dfBuses.itertuples()))

mdl.solve()

# Dislay solution
for b in dfBuses.itertuples():
    print(mdl.nbBus[b].solution_value," buses with ",b.size," seats");

• list integer_var_list

from docplex.mp.model import Model
from pandas import *

# Data

dfBuses = pandas.DataFrame({'size': [40,30], 'cost': [500,400]},
                      index = ['bus1', 'bus2'], columns=['size','cost'])

print(dfBuses)
print(dfBuses.shape[0])

nbKids=300

for b in dfBuses.itertuples():
    print("buses with ",b.size," seats cost ",b.cost)

mdl = Model(name='buses')

nbBusesTypes=dfBuses.shape[0]

#decision variables

mdl.nbBus = mdl.integer_var_list(nbBusesTypes,0,1000,name="nbBus")

# Constraint
mdl.add(sum(mdl.nbBus[b]*dfBuses.iloc[b][0] for b in range(0,nbBusesTypes)) >= nbKids)

# Objective
mdl.minimize(sum(mdl.nbBus[b]*dfBuses.iloc[b][1] for b in range(0,nbBusesTypes)))

msol=mdl.solve()

# Dislay solution
for b  in range(0,nbBusesTypes):
    print(msol[mdl.nbBus[b]]," buses with ",dfBuses.iloc[b][0]," seats");

regards

 

#CPLEXOptimizers
#DecisionOptimization