  Ground staff agents of airlines operate many jobs at airports such as
passengers check-in, planes cleaning, etc. Shift planning aims at building the
sequences of jobs operated by ground staff agents, and have been widely studied
given its impact on operating costs. As these jobs are closely related to
flights arrivals and departures, flights delays disrupt ground staff schedules,
which leads to high additional costs. Our goal is to provide a solution for
shift planning at Air France that takes into account these additional costs. We
therefore introduce a stochastic version of the shift planning problem that
takes into accounts the cost of disruptions due to delay, and a column
generation approach to solve it. The key element of our column generation is
the algorithm for the pricing subproblem, which we model as a stochastic
resource constrained shortest path problem. Numerical results on Air France
industrial instances prove the relevance of the shift planning problem and the
efficiency of the solution method. The column generation can solve to
optimality instances with up to two hundred and fifty jobs. Moving from the
deterministic problem to the stochastic one including delay costs enables to
reduce the total operating costs by 3% to 5% on our instances.
