Railway traffic is often afflicted by unforeseen perturbations causing so-called primary delays. As a result, impacted trains utilize some track sections later than planned. This may impact neighboring trains in that may not be allowed to proceed at their planned speed due to the onset of conflicts triggering restrictive signals. The consequent unplanned slow-downs or stops may cause delay propagation, as secondary delay. Dispatchers can counteract this by quick and effective train re-timing, re-routing and re-ordering decisions. In the literature, this is known as the real-time Railway Traffic Management Problem (rtRTMP). RECIFE-MILP is an optimization algorithm for the rtRTMP that may be used as a decision support tool for dispatchers. In the literature, the impact of rtRTMP algorithms on traffic is seldom assessed considering an actual possible deployment framework. This can be done considering a railway simulator to replace reality and using an optimization algorithm for making traffic management decisions. To further increase the realism of the assessment, a closed-loop framework can be set up, in which optimization is repeated periodically to take into account the actual traffic state as time evolves. In this work, we assess the performance of RECIFE-MILP when used in closed-loop with a state-of-the-art railway simulator named OpenTrack. We consider various infrastructures modeled microscopically, real timetables and realistic traffic perturbations. In particular, we perform a sensitivity analysis on the most relevant closed-loop meta-parameters, to understand their impact on the quality of the traffic management decisions implemented over time. For example, we vary the time horizon concerning the decisions to be taken and the frequency at which a new rtRTMP solution is to be computed. The results show how the best meta-parameter configuration depends on the characteristics of the case study considered.