Didier Keymeulen, Martine de Gerlache
Airline companies usually schedule their flights and crews well in advance to optimize their crew pools activities. Many events such as flight delays or the absence of a member require the crew pool rescheduling team to change the initial schedule (rescheduling). In this paper, we show that the neural network comparison paradigm applied to the backgammon game by Tesauro (Tesauro and Se(cid:173) jnowski, 1989) can also be applied to the rescheduling problem of an aircrew pool. Indeed both problems correspond to choosing the best solut.ion from a set of possible ones without ranking them (called here best choice problem). The paper explains from a math(cid:173) ematical point of view the architecture and the learning strategy of the backpropagation neural network used for the best choice prob(cid:173) lem. We also show how the learning phase of the network can be accelerated. Finally we apply the neural network model to some real rescheduling problems for the Belgian Airline (Sabena).