Each year weather-related airline delays result in hundreds of millions of dollars in additional fuel burn, maintenance, and lost revenue, not to mention passenger inconvenience. The current approaches for aircraft route planning in the presence of adverse weather still mainly rely on deterministic methods. In contrast, this work aims to deal with the problem using a Partially Observable Markov Decision Processes (POMDPs) framework, which allows for reasoning over uncertainty (including uncertainty in weather evolution over time) and results in solutions that are more robust to disruptions. The POMDP-based decision support system is demonstrated on several scenarios involving convective weather cells and is benchmarked against a deterministic planning system with functionality similar to those currently in use or under development.


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    Title :

    Dynamic Routing of Aircraft in the Presence of Adverse Weather Using a POMDP Framework



    Conference:

    Annual AIAA Aviation Forum and Exposition 2017 ; 2017 ; Denver, CO, United States


    Publication date :

    2017-06-05


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English




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