Docplex use for timeseries based variables

Question

I am trying to model an optimisation problem where each item in a list is essentially power generated at that hour. I am trying to minimise the amount of energy stored while still getting the same energy over the course of the time series, and as such am modelling it as a linear programming problem.

I am trying to add a constraint, but am running into problems. The important bits look like this:

var_1 = model.continuous_var_list(1000, name = 'var_1')
var_2 = model.continuous_var_list(1000, name = 'var_2')
var_3 = model.continuous_var_list(1000, name = 'var_3')

model.add_constraint(var_1[i] - var+2[i] == var_3[i] for i in range(1000))

Where I am trying to say that at timestep i, var_3 should always equal the difference between var_1 and var_2. Is there a good example for this kind of problem I can learn from, or does anyone have any advice? Either in Docplex, or a similar thing in CPLEX I can use to understand. This code results in the error:

Expecting constraint, got: <generator object Home.linearlySolve.<locals>.<genexpr> at 0x0000013F2BBBA940> with type: <class 'generator'>

Thanks!

maybe you should use normal for-loop with .add_constraint() inside this loop. — furas
– furas, Commented Mar 30 at 16:20
That would work, but add_constraints (with an S) is much more efficient in adding a batch of constraint — Philippe Couronne
– Philippe Couronne, Commented Mar 31 at 14:28

Alex Fleischer · Accepted Answer · 2025-03-30 16:27:57Z

0

You could use add_constraints with an "s"

from docplex.mp.model import Model

model = Model(name='model')

var_1 = model.continuous_var_list(1000, name = 'var_1')
var_2 = model.continuous_var_list(1000, name = 'var_2')
var_3 = model.continuous_var_list(1000, name = 'var_3')

model.add_constraints(var_1[i] - var_2[i] == var_3[i] for i in range(1000))

sol=model.solve(log_output=True)

for v in model.iter_continuous_vars():
    print(v," = ",v.solution_value)

works fine

answered Mar 30 at 16:27

Alex Fleischer

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