Disruption is a serious and common problem for the airline industry. High utilisation of aircraft and airport resources mean that disruptive events can have large knock-on effects for the rest of the schedule. The airline must rearrange their schedule to reduce the impact. The focus in this paper is on the Aircraft Recovery Problem. The complexity and uncertainty involved in the industry makes this a difficult problem to solve. Many deterministic modelling approaches have been proposed, but these struggle to handle the inherent variability in the problem. This paper proposes a multi-fidelity modelling framework, enabling uncertain elements of the environment to be included within the decision making process. We combine a deterministic integer program to find initial solutions and a novel simulation optimisation procedure to improve these solutions. This allows the solutions to be evaluated whilst accounting for the uncertainty of the problem. The empirical evaluation suggests that the combination consistently finds good rescheduling options.

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The optimisation of maintenance plans for complex systems involving many components is not an easy problem. Analytical and mathematical models are possible, but often need to make significant assumptions and are unable to look at the distribution of costs and failures. This paper discusses a project in which a discrete-time simulation model was added onto an existing optimisation model in order to go beyond just estimating the mean performance and give a better picture of the risk and variability involved with potential maintenance plans.

Input model bias is the bias found in the output performance measures of a simulation model caused by estimating the input distributions/processes used to drive it. When the simulation response is a nonlinear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is amongst the errors that arise. In this paper, we introduce a method that recalibrates the input parameters of parametric input models to reduce the bias in the simulation output. The proposed method is based on sequential quadratic programming with a closed form analytical solution at each step. An algorithm with guidance on how to practically implement the method is presented. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling error. Summary of Contribution: This paper furthers the understanding and treatment of input modelling error in computer simulation. We provide a novel method for reducing input model bias by recalibrating the input parameters used to drive a simulation model. A sequential quadratic programming approach with an explicit solution is provided to recalibrate the input parameters. The method is therefore computationally inexpensive. An algorithm outlining our proposed procedure is provided within the paper. An evaluation of the method shows the method successfully reduces input model bias and may also reduce the mean squared error caused by input modelling in the output of a simulation model.