cpoptimizer.cpp

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/**@file   cpoptimizer.cpp
 * @brief  contains method to solve a single cumulative condition via IBM ILOG CP Optimiter
 * @author Stefan Heinz
 */
 
/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
 
#ifdef WITH_CPOPTIMIZER
 
#include <ilcp/cp.h>
 
#include "cpoptimizer.h"
 
/** solve single cumulative condition using CP Optimizer */
SCIP_DECL_SOLVECUMULATIVE(cpoptimizer)
{
   IloEnv env;
 
   (*solved) = FALSE;
   (*infeasible) = FALSE;
   (*unbounded) = FALSE;
   (*error) = FALSE;
 
   try
   {
      int totaldemand;
      int v;
 
      IloModel model(env);
      IloIntervalVarArray jobs(env, njobs);
 
      IloCumulFunctionExpr cumulative = IloCumulFunctionExpr(env);
      IloNumExpr costs(env);
 
      totaldemand = 0;
 
      for( v = 0; v < njobs; ++v )
      {
         jobs[v] = IloIntervalVar(env);
 
         /* set bounds */
         jobs[v].setStartMin(ests[v]);
         jobs[v].setEndMax(lsts[v] + durations[v]);
 
         /* set job duration */
         jobs[v].setSizeMin(durations[v]);
         jobs[v].setSizeMax(durations[v]);
 
         /* add job to cumulative constraint  with corresponding demand */
         cumulative += IloPulse(jobs[v], demands[v]);
 
         if( objvals != NULL )
            costs += IloStartOf(jobs[v]) * objvals[v];
 
         totaldemand += demands[v];
      }
 
 
      if( totaldemand <= capacity && objvals == NULL )
      {
         for( v = 0; v < njobs; ++v )
            lsts[v] = ests[v];
 
         (*solved) = TRUE;
 
         return SCIP_OKAY;
      }
 
      /* add objective */
      IloObjective objective(env);
      objective.setExpr(costs);
      objective.setSense(IloObjective::Minimize);
 
 
      /* add cumulative constraint to the model */
 
      IloIntervalVar horizon(env);
      horizon.setStartMin(hmin);
      horizon.setEndMax(hmax);
      horizon.setSizeMin(hmax - hmin);
      horizon.setSizeMax(hmax - hmin);
 
      cumulative += IloPulse(horizon, totaldemand - capacity);
      model.add(cumulative <= totaldemand);
 
      IloCP cp(model);
 
      /* set time limit */
      cp.setParameter(IloCP::TimeLimit, timelimit);
 
      if( maxnodes >= 0 )
         cp.setParameter(IloCP::ChoicePointLimit, maxnodes);
 
      cp.setParameter(IloCP::LogVerbosity, IloCP::Quiet);
 
      cp.setParameter(IloCP::SearchType, IloCP::DepthFirst);
      cp.setParameter(IloCP::CumulFunctionInferenceLevel, IloCP::Extended);
      cp.setParameter(IloCP::NoOverlapInferenceLevel, IloCP::Extended);
      cp.setParameter(IloCP::Workers, 1); // Use only one CPU
 
      cp.solve();
 
      switch( cp.getStatus() )
      {
      case IloAlgorithm::Feasible:
      case IloAlgorithm::Optimal:
         /* collect optimal solution */
         for( v = 0; v < njobs; ++v )
         {
            ests[v] = cp.getStart(jobs[v]);
            lsts[v] = cp.getStart(jobs[v]);
         }
         (*solved) = TRUE;
         break;
      case IloAlgorithm::InfeasibleOrUnbounded:
         (*infeasible) = TRUE;
         (*unbounded) = TRUE;
         (*solved) = TRUE;
         abort();
         break;
      case IloAlgorithm::Infeasible:
         (*infeasible) = TRUE;
         (*solved) = TRUE;
         break;
      case IloAlgorithm::Unbounded:
         (*unbounded) = TRUE;
         break;
      case IloAlgorithm::Unknown:
      case IloAlgorithm::Error:
         (*error) = TRUE;
         break;
      }
   }
   catch( IloException& e )
   {
      SCIPerrorMessage("CP Optimizer Execution <%s>\n",e.getMessage());
      (*error) = TRUE;
   }
 
   env.end();
 
   return SCIP_OKAY;
}
 
#endif