On an airport, numerous activities are going on in parallel: aircrafts departing and landing, passengers checking in, luggage being loaded, … They depend on each other and there is only little tolerance for delays. Every delay will have consequences for the subsequent activity. On top of that there might arise additional disturbances such as lonely luggages or the need for de-icing (further slowing down the processes).


The person responsible for the management of all these activities is the airline operator. If disturbances arise the airline operator has to make a fast decision. S/he has to collect all information needed in order to make the best decision – on average within X minutes.

Currently this data collection process is extensive and inefficient as data is spread across different systems and tools, therefore working separately and independently from each other.


Opstimal wants to optimize the workflow for the airline operator. To do so the different data sources are combined within one data platform in order to

  1. provide more quickly a better understanding of the current situation for the airline operator.
  2. suggest the best options using a holistic optimization algorithm that can be adapted to personal priorities.

In the end the airline operator will find two displays: a situation display showing all disturbances and their consequences. And an option display showing possible options for actions which can be filtered by individual priorities (e. g. cost or time savings).


In order to bring together the spread data of diverse subsystems these subsystems must be understood.
To do so Opstimal is organized in different work packages in which the most important use cases are detected, data is connected and analysed:
work package 1: flight plan & trajectory,
work package 2: predictive maintenance,
work package 3: turnaround & crew.
In parallel, in work package 4 an algorithm is developed that holistically optimizes the combined data of the subsystems.