We address workforce optimization for ground handling operations at the airport, focusing on baggage loading and unloading. Teams of skilled workers have to be formed and routed across the apron to unload the baggage from the aircraft after a landing and to load it before take-off. Such tasks must be performed within time windows and require a team of workers with different skill levels. Furthermore, in practice, various factors such as travel times between tasks are subject to delays, e.g. from planes crossing the runway or apron blockage. The goal is to find a plan that is feasible in every scenario with respect to the available workforce and minimizes the sum of the task completion times. We formalize a variation of the workforce scheduling and routing problem, integrating team formation, hierarchical skills with downgrading, multiple trips, and different execution modes and incorporate stochastic travel times with a known probability distribution. We propose a solution approach based on branch-and-price and test it on real-world deterministic instances from a major European hub airport, enriched by simulated delays on travel times between tasks. We propose a model solved using a column generation approach. In the pricing problem, we generate tours of teams as shortest paths with constrained resources on a network and reduce its runtime using several acceleration strategies. In the Master Problem, we select an optimal set of tours that do not exceed the workforce availability. Preliminary results show that the proposed algorithm is able to produce good or optimal results for small and medium and reasonable results for large-sized instances, likewise for small or medium-sized measurable spaces.