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

Robust Optimization Approaches for Routing and Scheduling of Multi-Skilled Teams under Uncertain Job Skill Requirements