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The parameters are such as in the dataframe above. Can I iterate over this, and solve the model firstly for row 0 and then 1, ... etc.

  • 1
    $\begingroup$ if this is a pandas dataframe then just write a for loop in which an optimization model is run in each iteration and index your data with df.loc[iteration,column_indexer] $\endgroup$
    – PeterD
    Jun 5, 2022 at 9:46

1 Answer 1


See example in easy optimization with python

from docplex.mp.model import Model
import pandas as pd

buses = {'costBus40': [500, 550,600], 'costBus30': [400, 450,440],'nbKids': [300, 320,330]}
dfBusesScenarii = pd.DataFrame(data=buses)

for s in range(0,len(dfBusesScenarii)):

    mdl = Model(name='buses')
    nbbus40 = mdl.integer_var(name='nbBs40')
    nbbus30 = mdl.integer_var(name='nbBus30')

    mdl.add_constraint(nbbus40*40 + nbbus30*30 >= nbKids, 'kids')
    mdl.minimize(nbbus40*costBus40 + nbbus30*costBus30)

    print("if we need to bring ",nbKids," kids  to the zoo");
    print("with costs ",costBus40," and ",costBus30)
    print(int(nbbus40.solution_value)," buses 40 seats and ",int(nbbus30.solution_value), " buses 30 seats");
    print("cost = ",cost)

which gives

if we need to bring  300  kids  to the zoo
with costs  500  and  400
6  buses 40 seats and  2  buses 30 seats
cost =  3800.0
if we need to bring  320  kids  to the zoo
with costs  550  and  450
8  buses 40 seats and  0  buses 30 seats
cost =  4400.0
if we need to bring  330  kids  to the zoo
with costs  600  and  440
0  buses 40 seats and  11  buses 30 seats
cost =  4840.0

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