{'fraction_non_alphanumeric': 0.023890784982935155, 'fraction_numerical': 0.0, 'mean_word_length': 5.409836065573771, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A new framework for solving en-routes conflicts

The en-route conflict resolution problem has been modelled in many different ways, generally depending on the tools that were proposed to solve it. For instance, with purely analytic mathematical solvers, models tend to be very restrictive (constant speeds, linear trajectories\dots) to respect the inherent limitations of the technology. This paper introduces a new framework that separates the model from the solver so as to be able to: first, enhance the model with as many refinements (e.g. wind and trajectory uncertainties) as necessary to comply with operational constraints; second, compare different resolution methods on the same data, which is one of the crucial aspects of scientific research. To this aim, our framework can generate a benchmark of conflict resolution problems built with various scenarios involving a given number of aircraft, level of uncertainties and number of manoeuvres. We then compare two different optimization paradigms, Evolutionary Algorithm and Constraint Programming, which can efficiently solve difficult instances in near real time, to illustrate the usefulness of our approach.