Automated repair of input data for fleet assignment in a digital airline twin

Digitalization has great potential for making air transport more efficient and sustainable, e.g. by holistic optimization in order to improve the usage of resources. This applies in particular to processes within an airline. In this context, an automated decision-making tool with a high degree of integration and compatibility to different business strategies could enable airlines, politicians, and research institutions to assess various scenarios. For example, it could help determine how new aircraft types can be integrated into an airline’s fleet or how flight demand can be met with limited resources. For this purpose, a digital airline twin (DAT) is currently being created by the German Aerospace Center (DLR). One major difficulty is that the input data, such as airline schedules, which the DAT typically receives from databases, is often neither complete nor flawless. Hence, there is a need for automatic procedures for dealing with these imperfect data sets. This study presents two algorithms for making schedules periodic through the addition of repositioning legs. One approach is more profit-driven while the other one aims to stay as close to the original schedule as possible. Comparing and evaluating both, the second algorithm turned out to be the better option for the DAT as the necessary computational effort is smaller and the results are at least as good as those of the first algorithm.