P1. Decision support system for airline operation control hub centre (‘DiSpAtCH’)

The general objective of DiSpAtCH (Decision Support System for Airline Operation Control Hub Centre) is to elaborate on artificial intelligence technologies and how these technologies could efficiently support decision making in an Airline Operation Control Hub Centre (OCC) in unexpected or very complex situations.

The daily operation of airlines is often disrupted by unplanned events. As an airline it is therefore essential to operate an OCC to be able to react and mitigate any consequences from the initial disruption. The most challenging task is the information management task. This task includes monitoring, recognition and projection of relevant information out of all information available including current and future situations.

Today the decision making process mainly relies on the experience of the staff working in the OCC. Like in other industries, the desire of using Decision Support Tools (DST) based on machine learning (ML) algorithms is also increasing in the aviation industry. ML algorithms, like neural networks, need a large amount of data to be trained with. The focus of DiSpAtCH is to develop a DST which aims to help the staff in an OCC during disrupted situations. Therefore, three ML modules have been defined of which one aims to propose a suitable action/solution in a disrupted situation. To train the algorithm a database including information about disruptions as well as the implemented solutions from past disrupted situations is needed. Since these kinds of data are not available to researchers and often not recorded by airlines themselves, an approach was needed to get some data to start training algorithms and to validate that certain DST can be developed and support the disruption management process within an OCC. With a decision support system like DiSpAtCH the decisions within an OCC can be optimized which will result in fewer overall disruption cost.

DiSpAtCH provides an approach of using an airline simulation to generate generic operational data of an airline and its daily operations. Synthetic data are generated and ML algorithms are trained to predict actions/solutions for disrupted situations. A first validation shows that a four step classification process including two neural networks can be used to predict actions/solutions in disrupted situations with an accuracy of around 95% and therefore reduce the overall disruption cost by 61% compared to randomly selected actions/solutions.