  In airport operations, optimally using dedicated personnel for baggage
handling tasks plays a crucial role in the design of resource-efficient
processes. Teams of workers with different qualifications must be formed, and
loading or unloading tasks must be assigned to them. Each task has a time
window within which it can be started and should be finished. Violating these
temporal restrictions incurs severe financial penalties for the operator. In
practice, various components of this process are subject to uncertainties. We
consider the aforementioned problem under the assumption of stochastic travel
times across the apron. We present two binary program formulations to model the
problem at hand and solve it with a Branch-Price-Cut-and-Switch approach, in
which we dynamically switch between two master problem formulations.
Furthermore, we use an exact separation method to identify violated rank-1
Chv\'atal-Gomory cuts and utilize an efficient branching rule relying on task
finish times. We test the algorithm on instances generated based on real-world
data from a major European hub airport with a planning horizon of up to two
hours, 30 flights per hour, and three available task execution modes to choose
from. Our results indicate that our algorithm is able to significantly
outperform existing solution approaches. Moreover, an explicit consideration of
stochastic travel times allows for solutions that utilize the available
workforce more efficiently, while simultaneously guaranteeing a stable service
level for the baggage handling operator.
