The en route conflict resolution problem has been modeled in many different ways, generally depending on the tools proposed to solve it. For instance, with purely analytic mathematical solvers, models tend to be very restrictive 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 as necessary to comply with operational constraints; second, compare different resolution methods on the same data, which is a crucial aspect of scientific research.To this aim, our framework generates a benchmark of conflict resolution problems built with various scenarios involving different numbers of aircraft, levels of uncertainties and numbers of maneuvers. 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.into account all these uncertainties to choose the best trajectories in terms, first, of safety and then, efficiency. These certainties probably explain why the short-term traffic resolution system still relies on human expertise and is not yet automated.Much research has been done on conflict detection and resolution and many papers present models that are so impractical that they strengthen the readers' beliefs that automating the conflict detection and resolution task is unrealistic in the near-term. For example, the approach using repulsive forces described in [Zeghal, 1993] or the B-spline approximation model of [Delahaye et al., 2010] are very interesting on a mathematical level but could hardly be implemented in an operational context. They suppose continuous heading changes, which Flight Management Systems (FMS) are unable to exploit, and do not take uncertainties into account. Pallottino's approach [Pallottino et al., 2002] using mixed integer linear programming (as [Vela et al., 2009, Alonso-Ayuso et al., 2011, Rey et al., 2012) relies on constant speed trajectories that are changed all at once. None of these approaches could deal with realistic trajectory models able to handle evolutive aircraft or trajectory uncertainties.Other approaches like [Durand et al., 1996, Granger et al., 2001 propose to solve conflicts using Evolutionary Algorithms, relying on more realistic models built upon the Base of Aircraft Data (BADA) developed and maintained by EUROCONTROL. These models introduce uncertainties on aircraft speed, climb and descent rate, thus the solver needs to compute many alternative trajectories in real time. Nevertheless, the solver is quite efficient as it can handle complete days of traffic in the European airspace. These algorithms, however, are difficult to compare with other methods because the conflict detection is embedded in the solver. This problem also occurs in Erzberger's approach [Erzberger, 1997], where most of the expertise is focused on the trajectory and maneuver model. Once more, th...