1

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.

Ashkan Rezaee
  • 11
  • 3

1 Answers1

0

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]
Alex Fleischer
  • 9,276
  • 2
  • 12
  • 15
  • Thank you Alex. from the last part, I understand that the equal value of the constraints is changed by mdl.get_constraint_by_name("kids").rhs=nbKidsOptions[i] But my case is different. I need to add the same constraint with different bounds. How can I implement it? – Ashkan Rezaee Sep 07 '20 at 13:25
  • Hi, other examples of incremental changes in https://github.com/AlexFleischerParis/zoodocplex/blob/master/zooincremental.py – Alex Fleischer Sep 07 '20 at 14:44
  • You could wrap your model generation code in a function that takes SetAlt as argument. Then generate as many models from these sets and run populate on them. – Philippe Couronne Sep 07 '20 at 15:10
  • With this approach, I have an issue. My solutions become very much similar to each other which I don't want this to happen. How can I make sure solutions from different models are much different than each other? – Ashkan Rezaee Sep 07 '20 at 15:53
  • You might look at answers to this question, https://stackoverflow.com/questions/63781282/obtaining-different-solutions-on-solving-a-cplex-model-many-times, which explains how to use the diversity filter – Philippe Couronne Sep 08 '20 at 08:23