A multistage stochastic programming model for the network air cargo allocation under capacity uncertainty

We propose a multistage stochastic programming model to optimally allocate cargo to the passengers network in order to maximize profit, taking into account incomes, costs and penalties for not delivering cargo that was previously accepted. Flights have a discrete number of possible capacity outcomes, with known probabilities, and uncertainty is represented by a scenario tree. The resulting problem is a large-scale linear program, and we use decomposition techniques to solve it, leveraging on the problem structure in order to be able to find good quality solutions. Our numerical experiments are based on a real network of a major commercial airline.