Combining robustness and recovery for airline schedules
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Airline schedules are rarely performed as planned because of irregularities such as delays, unpredicted maintenances or bad weather conditions. Recover from a disrupted schedule as quickly as possible is a hard and high priority problem for practitioners: ...
We consider the recovery of an airline schedule after an unforeseen event, called {\em disruption}, that makes the planned schedule unfeasible. In particular we consider the aircraft recovery problem for a heterogeneous fleet of aircrafts, made of regular ...
We propose a multiplicative specification of a discrete choice model that renders choice probabilities independent of the scale of the utility. The scale can thus be random with unspecified distribution. The model mostly outperforms the classical additive ...
We propose a multiplicative specification of a discrete choice model that renders choice probabilities independent of the scale of the utility. The scale can thus be random with unspecified distribution. The model mostly outperforms the classical additive ...
In a distributed application, high-availability of a critical online service is ensured despite failures by duplicating the vital components of the server. Whilst guaranteeing the access to the server at all times, duplication requires particular care, so ...
This paper deals with problems arising in the sensitivity analysis for fluid-structure interaction systems. Our model consists of a fluid described by the incompressible Navier-Stokes equations interacting with a solid under large deformations. We obtain a ...
An important problem in computer vision is the reconstruction problem which consists in reconstructing a three dimensional surface on the base of different images of the surface. The classical formulations for solving this problem work with the so called ≪ ...
Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N3) where N is the data set size. In ...
Given n points in the s-dimensional unit cube, we consider the problem of finding a subinterval of minimum or maximum volume that contains exactly k of the n points. We give integer programming formulations of these problems and techniques to tackle their ...
Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N3) where N is the data set size. In ...
