Report on State-of-the-Art of Relevant Concepts (EU-H2020-Project SlotMachine)

This deliverable is part of a project that has received funding from the SESAR Joint Undertaking under grant agreement No 890456 under European Union’s Horizon 2020 research and innovation programme. This document provides the analysis of the state of the art regarding evolutionary algorithms related to SESAR ER project SlotMachine. SlotMachine employs blockchain technology and secure multi-party computation to extend the existing User-Driven Prioritisation Process (UDDP) solution with the possibility to keep private the participating airlines’ confidential information such as the cost structure of flights. Technology will allow for secure, auditable transactions without the need for a central broker, whereby stakeholders will be able to enter slot swapping transactions without disclosing information to other participants. By demonstrating the feasibility of a privacy-preserving platform for swapping ATFM slots, the foundation can be laid for the development of a product that will be an essential element in the aviation industry in the future. It contributes to better use of existing resources at airports, higher efficiency of airlines, lower emissions, and shorter delays for passengers. In this deliverable, we experimentally examine different evolutionary algorithms with varying parameters regarding the algorithms’ suitability to find solutions to the SlotMachine flight prioritization problem for maximizing airspace users’ overall utility. For the experiments, we generated synthetic test data to model different scenarios regarding the airspace users’ preferences. To this end, we implemented a non-privacy-preserving prototype for the SlotMachine Heuristic Optimizer, which employs commonly available open-source software frameworks, to experiment with evolutionary algorithms. First, the Jenetics framework served to implement different genetic algorithms in Java for flight prioritization, providing different variants of mutators and selectors which can be used. Second, OptaPlanner provides implementations of heuristic local search algorithms, e.g., hill climbing, simulated annealing, and tabu search, which can be leveraged to solve the SlotMachine flight prioritization problem. Keywords: Air traffic flow management, evolutionary algorithm, genetic algorithm, assignment problem, privacy Technical Report, Projekt SLOTMACHINE, Deliverable D4.1, CORDIS - EU Research Results, Projekt-DOI: https://doi.org/10.3030/890456, 2022. Project on CORDIS: https://cordis.europa.eu/project/id/890456/results/de