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An optimization–simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

Abstract : 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.
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https://hal-enac.archives-ouvertes.fr/hal-03094622
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Soumis le : lundi 4 janvier 2021 - 13:24:22
Dernière modification le : mardi 19 octobre 2021 - 11:02:55

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Paolo Scala, Miguel Mujica Mota, Cheng-Lung Wu, Daniel Delahaye. An optimization–simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty. Transportation research. Part C, Emerging technologies, Elsevier, 2021, 124, pp.102937. ⟨10.1016/j.trc.2020.102937⟩. ⟨hal-03094622⟩

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