This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.
Recently KLM has revealed the plan to downsize the full-freight cargo fleet in Schiphol Airport, for that reason it is important for the company and the airport to explore the consequences of moving the cargo transported by the full freighters into the bellies of the passenger flights. The consequences of this action in terms of capacity and requirements are still unknown for the stakeholders. The current study illustrates that once the freighters are phased out, the commercial traffic needs to adjust mainly their load factors in order to absorb the cargo that was previously transported by the full freighters. The current model is a version that includes the airside operation of the airport and also the vehicle movement which allows addressing the uncertainties of the operation as well as the limitations and potential problems of the phasing-out action.
