How to change Cplex model during solution generation?

Question

I have a Cplex model with several constraints and a solution pool. One of my constraint is :

 R_alt=[i for i in R if i not in SetAlt]
        model.add_constraints((model.sum(x[i, j] for j in R2 ) == 2 for i in R_alt),"6C" )
        model.add_constraints((x[i, n1-4] ==x[i, n1-2]  for i in R_alt ),"7C" )

SetAlt is a set of 2 values that will be removed from R_alt before making the constraint. I need these 2 values to be picked randomly by the cplex for each solution. In other words, I need cplex to change the model on this constraint during solution pool generation.

For example if I have 6C on R_alt=[0,1,2,3,6,7,8] in one solution, I get R_alt=[0,2,3,4,5,6,8] in another solution.

Before, I was using python random for picking this SetAlt but the problem was that I had the same SetAlt in all solutions.

Answer 1

in https://github.com/AlexFleischerParis/zoodocplex/blob/master/zoomontecarlooptimization.py

import random
import math

random.seed(1)

from docplex.mp.model import Model

# original model

nbKids=300
mdl = Model(name='buses')
nbbus40 = mdl.integer_var(name='nbBus40')
nbbus30 = mdl.integer_var(name='nbBus30')
costBus40=500.0;
costBus30=400.0;
mdl.add_constraint(nbbus40*40 + nbbus30*30 >= nbKids, 'kids')
mdl.minimize(nbbus40*costBus40 + nbbus30*costBus30)

nbSamples=20
nbMaxKidsAbsent=30;

nbKidsLess=[random.randint(0,nbMaxKidsAbsent) for i in range(0,nbSamples)]
nbKidsOptions=[nbKids-nbKidsLess[i] for i in range(0,nbSamples)]

#Monte Carlo optimization

totalCost=0.0;
for i in range(0,nbSamples):
    
   mdl.get_constraint_by_name("kids").rhs=nbKidsOptions[i]
   mdl.solve()
   cost=mdl.solution.get_objective_value()
   totalCost+=cost
   print("if we need to bring ",nbKidsOptions[i]," kids  to the zoo");
   print("cost = ",cost)

print()   
averageCost=1/nbSamples*totalCost


print("------------------------------");
print("average cost = ",math.ceil(averageCost));

you may find an example of changing a constraint and solving again:

mdl.get_constraint_by_name("kids").rhs=nbKidsOptions[i]