Ground staff shift planning under delay uncertainty at Air France

Abstract: 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.