Reading the Data

> Discrete Optimization > Early Tardy Scheduling > Reading the Data

Reading the Data

As you see in Code Sample 19.1, the first part of this application reads data from a file and fills two 10x10 matrices:

resourceIndex contains the indices of the resources;
duration contains the duration of each activity.

    IloNumArray jobDueDate(env, nbJob);
    IloNumArray jobEarlinessCost(env, nbJob);
    IloNumArray jobTardinessCost(env, nbJob);

    NumMatrix   duration(env, nbJob);
    for(j = 0; j < nbJob; j++) {
      duration[j] = IloNumArray(env, nbResource);
    for (i = 0; i < nbResource; i++) {
        IloInt resourceIndex;
        f >> resourceIndex;
        f >> duration[j][i];
        activityOnAResource[resourceIndex].add(i);
      }
      f >> jobDueDate[j];
      f >> jobEarlinessCost[j];
      f >> jobTardinessCost[j];
    }

Code Sample 19.1 Reading data for early tardy scheduling

Each line in the data file corresponds to an array in the matrix and thus represents all the information about activities for a given job.

For each job, other arrays contain further information from the data file:

jobDueDate contains the due date for each job;
jobEarlinessCost contains the penalty for being too early for each job;
jobTardinessCost contains the penalty for being too late for each job.

The matrix activityOnAResource contains the sets of activities that must be scheduled on the same resource. This information will be used to state resource constraints.

IntMatrix   activityOnAResource(env, nbResource);
    for(i = 0; i < nbResource; i++) {
      activityOnAResource[i] = IloIntArray(env);
    }

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