For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.
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This article investigates the aesthetic advice posted by temporary employment agencies on their websites. These agencies organise a substantial part of the Dutch labour market and they are known to apply exclusionary practices in their strategies of recruitment and selection in order to meet employers’ preferences. This article sheds light on (1) the content of the advice; (2) how it legitimises the importance of aesthetics for finding work; and (3) in what ways the advice serves the purposes of the agencies. An in-depth content analysis illustrates how the advice has the potential to reproduce exclusions, thus helping employment agencies adhere to employers’ exclusionary requests. Creating online content that generates traffic to the websites in this case causes a circular logic in which the importance of aesthetics is self-reinforcing. The study illustrates that the seemingly neutral and empty advice posted on websites may enforce exclusions in the temporary work labour market.
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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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