One of the most promising ways to reduce emissions at airports is by towing aircraft instead of taxiing with their main engines, also known as dispatch towing. One of the airports most involved with this concept is Amsterdam Airport Schiphol (AMS), as it has an emission-free target for 2030. One of the challenges with this concept is to optimize the assignment of Electric Towing Vehicles (ETVs) to maximize the effectiveness. The developed model can assign ETVs to flights and charging moments for the tactical planning phase, minimizing fuel consumption, charging cost and number of chargers. The results of the model are illustrated for two peak days at AMS. Both a small and large fleet of ETVs are assigned on both days for a northbound and southbound runway operation. The total fuel cost savings for the small fleet are 25% and 45% for the large fleet, which are similar on both days. On both days, outbound flights are the preferred direction to be towed due to the distribution of towing times. The savings per ETV are highest for a small fleet and decrease until all flights are towed. Furthermore, the load on the charging infrastructure at AMS for different fleet sizes shows what average and peak power can be expected. It is shown that ETV utilization and computation time can be improved significantly, by implementing costs on time and introducing utilization and symmetry constraints. However, with the important limitation that these improvements are observed only for small planning horizons. Finally, a sensitivity analysis on charging power showed that increasing the charging rate has a positive impact on both fuel cost savings and the minimum number of chargers required. In conclusion, this study shows the potential impact of dispatch towing at AMS in terms of fuel savings, charging infrastructure and operational challenges.

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.