  We consider a combined problem of teaming and scheduling of multi-skilled
employees that have to perform jobs with uncertain qualification requirements.
We propose two modeling approaches that generate solutions that are robust to
possible data variations. Both approaches use variants of budgeted uncertainty,
where deviations in qualification requirements are bounded by a constraint. In
the first approach, we aggregate uncertain constraints to ensure that the total
number of job qualifications present at a job is not less than a worst-case
value. We show that these values can be computed beforehand, resulting in a
robust model with little additional complexity compared with the nominal model.
In our second approach, we bound the overall qualification deviation over all
jobs. While this approach is more complex, we show that it is still possible to
derive a compact problem formulation by using a linear programming formulation
for the adversarial problem based on a dynamic program. The performance of both
approaches is analyzed on a test bed of instances which were originally
provided for a deterministic problem version. Our experiments show the
effectiveness of the proposed approaches in the presence of data uncertainty
and reveal the price and gain of robustness.
