Niklaus Eggenberg

Constraint-specific recovery network for solving airline recovery problems

In this paper, we consider the recovery of an airline schedule after an unforeseen event called disruption, making the planned schedule infeasible. We present a modeling framework that allows the consideration of operational constraints within a Column Generation (CG) scheme. We introduce the general concept of recovery network, generated for each individual unit of the problem, and show how unit-specific constraints are modeled using resources. We fully illustrate the concept by solving the Aircraft Recovery Problem (ARP) with maintenance planning, we give some insights into applying the model to the Passenger Recovery Problem (PRP) and we present computational results on real data.

Uncertainty feature optimization: An implicit paradigm for problems with noisy data

In this work, we present a new framework to cope with problems due to uncertainty. We consider the uncertainty implicitly with helps of Uncertainty Feature Optimization (UFO), i.e. we transform the initial problem to a multi-objective problem. The aim is to increase structural properties of a solution that are known to improve its robustness or its recoverability.