Airlines have to achieve profitability while subject to highly uncertain factors, such as weather, varying demand, maintenance events, congestion, etc. The interplays among these factors are complex and large amounts of information are ignored due to difficulties in processing big data sets and finding useful correlations. This paper presents a method to better utilize existing sources of weather forecast and payload data for achieving more efficient flights.The prediction of weather conditions has a high impact on flight efficiency, especially for long-haul flights. Despite there being many flight plan optimizers in the market, most of them rely on deterministic weather forecast, which entails the prediction of a single temporal series of weather conditions, but there are alternatives to this approach. We explore the use of stochastic weather forecast, which presents several distinct weather predictions for the same time horizon, forming a so-called ensemble forecast, where the value of a forecast parameter in each of ensemble member is different. So far, ensemble forecasts have been very little exploited for flight planning purposes.Even without using ensemble forecasts, airlines carry out lots of statistical analyses based on past data, and devise effective policies for advising how much fuel and payload an aircraft should carry and how much of time buffer should be used in the schedule. But these buffers can be further reduced by doing forward-looking stochastic optimization. In this context, the use of ensemble forecast allows to select a trajectory that optimizes the expected outcome of a flight for an array of scenarios, instead of optimizing for a single one. Besides the weather, aircraft payload is another considerable source of uncertainty due to the lack of actual measurement of passenger and carry-on baggage weights.We tested stochastic optimization, first with the objective of optimizing single flights, then with the objective of optimizing whole schedules. In one of the experiments for optimizing single flights, it was observed that, in 55.8% of the cases, stochastic optimization outperforms conventional optimization in terms of fuel consumption; in only 0.4% of the cases, conventional optimization wins; and, in the remaining 43.8% of the cases, they achieve equal results. The experiments with stochastic payload optimization demonstrated that the use of payload uncertainty can squeeze a bit more fuel savings from the flight plan outcomes. But the use of this technology is not driven only by reducing overall fuel consumption. One optimization criterion can be the minimization of diversions or fuel emergencies, that is, choosing the candidate that minimizes the maximum fuel consumption (minimax). In a project derived from this system, the optimization of whole flight schedules is performed, and its benefits were assessed.


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    Title :

    Stochastic Flight Plan Optimization




    Publication date :

    2023-10-01


    Size :

    1669663 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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