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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2306.09836 |
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| _version_ | 1866911486884970496 |
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| author | Wu, Haochen Estes, Alexander S. Li, Max Z. |
| author_facet | Wu, Haochen Estes, Alexander S. Li, Max Z. |
| contents | Ground Delay Programs (GDPs) mitigate demand-capacity imbalances by holding flights on the ground when an airport's arrival capacity is reduced, thereby reducing costly airborne holding. A central challenge is that day-to-day demand-capacity balancing relies on accurate predictions of airport capacities. However, these predictions are deeply uncertain: forecast errors, operational disruptions, and climate change-driven shifts in weather severity can induce distribution shifts in capacity outcomes. Thus, policies optimized for a single predicted distribution may be brittle out of sample.
We address this challenge by developing a \emph{distributionally robust} framework for the single airport ground holding problem (dr-SAGHP). We also propose a method integrates Kelly's cutting plane method with the integer L-shaped method, and that is applicable more broadly to two-stage distributionally robust integer programs with relatively complete recourse and continuous second-stage decision variables. Our method includes a novel dual bisection and primal recovery algorithm that makes use of the structure of the distributionally robust integer program in order to quickly generate subgradients required by Kelly's cutting plane method. In computational experiments, our proposed algorithm delivers up to two orders-of-magnitude speedups compared to solving the convex reformulation directly, while maintaining negligible optimality gaps.
We generate capacity scenarios via Gaussian process regression and evaluate out-of-sample performance by perturbing the posterior mean and variance. The numerical experiment results show that dr-SAGHP delivers significant out-of-sample gains under moderate-to-severe shifts, improving the resilience and effectiveness of GDP decision-making under capacity uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_09836 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distributionally Robust Airport Ground Holding Problem under Wasserstein Ambiguity Sets Wu, Haochen Estes, Alexander S. Li, Max Z. Optimization and Control Ground Delay Programs (GDPs) mitigate demand-capacity imbalances by holding flights on the ground when an airport's arrival capacity is reduced, thereby reducing costly airborne holding. A central challenge is that day-to-day demand-capacity balancing relies on accurate predictions of airport capacities. However, these predictions are deeply uncertain: forecast errors, operational disruptions, and climate change-driven shifts in weather severity can induce distribution shifts in capacity outcomes. Thus, policies optimized for a single predicted distribution may be brittle out of sample. We address this challenge by developing a \emph{distributionally robust} framework for the single airport ground holding problem (dr-SAGHP). We also propose a method integrates Kelly's cutting plane method with the integer L-shaped method, and that is applicable more broadly to two-stage distributionally robust integer programs with relatively complete recourse and continuous second-stage decision variables. Our method includes a novel dual bisection and primal recovery algorithm that makes use of the structure of the distributionally robust integer program in order to quickly generate subgradients required by Kelly's cutting plane method. In computational experiments, our proposed algorithm delivers up to two orders-of-magnitude speedups compared to solving the convex reformulation directly, while maintaining negligible optimality gaps. We generate capacity scenarios via Gaussian process regression and evaluate out-of-sample performance by perturbing the posterior mean and variance. The numerical experiment results show that dr-SAGHP delivers significant out-of-sample gains under moderate-to-severe shifts, improving the resilience and effectiveness of GDP decision-making under capacity uncertainty. |
| title | Distributionally Robust Airport Ground Holding Problem under Wasserstein Ambiguity Sets |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2306.09836 |