In response to the growing demand for air travel, major airports are approaching critical thresholds in their infrastructure capacity. As the transportation sector continues to expand, it is increasingly important to address environmental concerns that arise from aspects, such as noise annoyance and fuel consumption. This paper aims to enhance the existing Flexible Runway Scheduling Model (FRSM) by integrating a tabu search algorithm with Receding Horizon Control (RHC), introducing non-linear noise restrictions, and implementing more sophisticated fuel burn modeling. The main goal is to evaluate how certain improvements affect the FRSM. To achieve this, a methodology has been developed that uses a multi-objective tabu search algorithm to minimize both fuel consumption and noise annoyance while assigning flights to runways. This study provides a comprehensive analysis of Amsterdam Airport Schiphol (AAS) across different scenarios, ranging from a 1.5-hour flight schedule to a full-day simulation, revealing significant findings. For the 1.5-hour and six-hour scenarios, the tabu search algorithm achieves a 55% and 87.3% reduction in computational time with marginal losses of 0.73% and 0.19% in solution accuracy for fuel burn optimization. Throughout all scenarios, the tabu search algorithm consistently results in a reduction of highly annoyed individuals ranging from 2.14% up to 62.5% compared to the existing FRSM, demonstrating its effectiveness. Moreover, the algorithm minimizes the impact on the flight schedule in terms of delay. Notably, as the flight schedule length increases, the performance of the tabu search algorithm improves compared to the existing FRSM. A sensitivity analysis optimization horizon indicates a positive effect on results, albeit with an associated computational cost. In conclusion, this study showcases the positive impacts of the remodeled FRSM, enabling a faster and more accurate trade-off. The research findings provide valuable insights for optimizing runway scheduling at major airports while balancing efficiency gains with environmental considerations.