Originally posted by: Cplex

Hi,

you could have a look at the example

CPLEX_Studio1261\opl\examples\opl\sched_stochastic_jobshop

regards

Originally posted by: Cplex

Thanks, but they're using the CP optimizer in this example. I'm currently working with the CPLEX optimizer, because it's not a complex problem.
Is there a possibility to put the objective function in a forall or while loop (in CPLEX)?

Thanks in advance!

Hi,

yes you can use the objective in a forall:

dvar float obj;

minimize obj;
subject to
{
forall(i in 1..3) obj>=i;
}

regards

Originally posted by: Cplex

Yes, but I need to use the minimization of the objective within the forall loop. Is that possible as well?

Hi,

do you want to minimize one objective or a set of objectives ?

If the latter, do you want to minimize on average or in the worst case ?

regards

Originally posted by: Cplex

I have to minimize a set of objectives. There are several scenarios and I have to minimize the costs for each of the scenarios. So it's neither the average nor the worst case.

Hi

but the decision variables are per scenario or shared by all scenarii ?

regards

Originally posted by: Cplex

The decision variables are per scenario.

Hi,

then this small example that I derived from scalableWarehouse could help you:

execute
{
cplex.epgap=0.01;
}

int nbScenarii=10;
range scenarii=1..nbScenarii;
int Fixed        = 10;
int NbWarehouses = 50;
int NbStores     = 200;

assert( NbStores > NbWarehouses );

range Warehouses = 1..NbWarehouses;
range Stores     = 1..NbStores;
int Capacity[w in Warehouses] =
  NbStores div NbWarehouses +
  w % ( NbStores div NbWarehouses );
int SupplyCost[scenario in scenarii][s in Stores][w in Warehouses] =
  1 + ( ( s + 10 * w ) % 100 ) + scenario*20;
dvar int Open[scenarii][Warehouses] in 0..1;
dvar float Supply[scenarii][Stores][Warehouses] in 0..1;
dexpr int TotalFixedCost[scenario in scenarii] = sum( w in Warehouses ) Fixed * Open[scenario][w];
dexpr float TotalSupplyCost[scenario in scenarii] = sum( w in Warehouses, s in Stores )  SupplyCost[scenario][s][w] * Supply[scenario][s][w];
minimize sum(scenario in scenarii)
(TotalFixedCost[scenario] + TotalSupplyCost[scenario]);

subject to {
forall(scenario in scenarii)
  {

  forall( s in Stores )
    ctStoreHasOneWarehouse:
      sum( w in Warehouses )
        Supply[scenario][s][w] == 1;
  forall( w in Warehouses )
    ctOpen:
      sum( s in Stores )
        Supply[scenario][s][w] <= Open[scenario][w] * Capacity[w];
        
      }        
}

regards

Originally posted by: Cplex

Yes, now it's working. Thank you very much!

Originally posted by: Cplex

Now I'm facing another problem. In this case I can't minimize or maximize the sum because the runtime of the model is getting too large.

My problem looks more or less like that:

forall (k in K) {

     z[k] == 0;

            forall (l in K| l > k) {

                     z[l] == 0;

                        solve model

                           z[l] == 1;   

                           z[k] == 1; }}                                           (after solving the model, before solving the model for the next k)

How can I do solve the model inside such a loop?