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Robust flight schedules with stochastic programming
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AI
DOI：10.1007/s10479-021-04214-w.png)
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Abstract
En 中文
Limiting flight delays during operations has become a critical research topic in recent years due to their prohibitive impact on airlines, airports, and passengers. A popular strategy for addressing this problem considers the uncertainty of day-of-operations delays and adjusts flight schedules to accommodate them in the planning stage. In this work, we present a stochastic programming model to account for uncertain future delays by adding buffers to flight turnaround times in a controlled manner. Specifically, our model adds slack to flight connection times with the objective of minimizing the expected value of the total propagated flight delay in a schedule. We also present a parallel solution framework that integrates an outer approximation decomposition method and column generation. Further, we demonstrate the scalability of our approach and its effectiveness in reducing delays with an extensive simulation study of five different flight networks using real-world data.
Keywords:
Airline planning
Robust scheduling
Stochastic programming
Column generation
L-shaped method
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