Abstract
AbstractScheduling aircraft turnarounds at airports requires the coordination of several organizations, including the airport operator, airlines, and ground service providers. The latter manage the necessary supplies and teams to handle aircraft in between consecutive flights, in an area called the airport ‘apron’. Divergence and conflicting priorities across organizational borders negatively impact the smooth running of operations, and play a major role in departure delays. We provide a novel simulation-optimization approach that allows multiple service providers to build robust plans for their teams independently, whilst supporting overall coordination through central scheduling of all the involved turnaround activities. Simulation is integrated within the optimization process, following simheuristic techniques, which are augmented with an efficient search driving mechanism. Two tailored constraint-based feedback routines are automatically generated from simulation outputs to constrain the search space to solutions more likely to ensure plan robustness. The two simulation components provide constructive feedback on individual routing problems and global turnaround scheduling, respectively. Compared to the state-of-the-art approach for aircraft turnaround scheduling and routing of service teams, our methodology improves the apron’s on-time punctuality, without the need for the involved organizations to share sensitive information. This supports a wider applicability of our approach in a multiple-stakeholder environment.
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Reference55 articles.
1. ACI (2020) Aci reveals top 20 airports for passenger traffic, cargo, and aircraft movements. https://aci.aero/news/2020/05/19/aci-reveals-top-20-airports-for-passenger-traffic-cargo-and-aircraft-movements/. Accessed: 22.07.2021.
2. Ball, M., Barnhart, C., Nemhauser, G., & Odoni, A. (2007). Air transportation: Irregular operations and control. Handbooks in operations research and management science, 14, 1–67.
3. Beldiceanu, N, Carlsson, M, & Rampon, JX. (2012). Global constraint catalog, 2nd edition (revision a). Tech. Rep. 2012:03. Computer Systems Laboratory.
4. Bello, I., Pham, H., Le, QV., Norouzi, M., & Bengio, S. (2017). Neural combinatorial optimization with reinforcement learning. https://openreview.net/pdf?id=Bk9mxlSFx
5. Bengio, Y., Lodi, A., & Prouvost, A. (2021). Machine learning for combinatorial optimization: A methodological tour d’horizon. European Journal of Operational Research, 290(2), 405–421. https://doi.org/10.1016/j.ejor.2020.07.063, https://www.sciencedirect.com/science/article/pii/S0377221720306895
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