دانلود رایگان مقاله زمان بندی باند فرودگاه مقاوم در شرایط نامطمئن

abstract

The runway is the main element that combines airside and groundside of the ATM System. Thus, it is crucial to develop efficient models and planning algorithms for its effective usage. The best planning algorithm, however, is useless if the resulting plans cannot be implemented in the real world. This often happens because the input data of the planning algorithms face disturbances or changes over time, respectively. For example, an estimated time of arrival/departure of an aircraft may be changed. It is usually not certain for the next ten hours. In this work, we study the runway scheduling problem under uncertain conditions. First, we present mathematical optimization models that ignore uncertainties. In the most effective approach, we compute for every discretized point in time whether an aircraft is scheduled and if so, which one is. Then, in each planning step we take uncertainties into account. We then apply different robust optimization methods in order to devise solution approaches that lead to stable plans. These optimization approaches are integrated into a simulation tool and evaluated in different traffic scenarios. The Monte-Carlo simulations for a mixed-mode runway system show that our robust approaches result in fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, we show that protection against uncertainties by using robust optimization indeed leads to considerably more stable plans.

7. Conclusion

The goal of this work was to study runway scheduling when explicit knowledge of aircraft uncertainty is available. To this end, we used a time-indexed optimization model for the mixed-mode runway scheduling problem that is able to cope with uncertainties in the input data. Using this model, we set-up a simulation approach in which we determined optimum schedules in each time step. Especially, we studied the question whether the robust approach leads to more stable plans with fewer go-arounds and departure slot losses.