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Constraint-based robust planning and scheduling of airport apron operations through simheuristics

Author

Listed:
  • Yagmur S. Gök

    (University of Edinburgh)

  • Silvia Padrón

    (TBS Education)

  • Maurizio Tomasella

    (University of Edinburgh)

  • Daniel Guimarans

    (Amazon)

  • Cemalettin Ozturk

    (Process, Energy and Transport Engineering)

Abstract

Scheduling 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.

Suggested Citation

  • Yagmur S. Gök & Silvia Padrón & Maurizio Tomasella & Daniel Guimarans & Cemalettin Ozturk, 2023. "Constraint-based robust planning and scheduling of airport apron operations through simheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 795-830, January.
  • Handle: RePEc:spr:annopr:v:320:y:2023:i:2:d:10.1007_s10479-022-04547-0
    DOI: 10.1007/s10479-022-04547-0
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    References listed on IDEAS

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    1. Silvia Padrón & Daniel Guimarans, 2019. "An Improved Method for Scheduling Aircraft Ground Handling Operations From a Global Perspective," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(04), pages 1-25, August.
    2. Jesica Armas & Angel A. Juan & Joan M. Marquès & João Pedro Pedroso, 2017. "Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1161-1176, October.
    3. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    4. Zhao, Peixin & Han, Xue & Wan, Di, 2021. "Evaluation of the airport ferry vehicle scheduling based on network maximum flow model," Omega, Elsevier, vol. 99(C).
    5. Alex Grasas & Angel A Juan & Helena R Lourenço, 2016. "SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization," Journal of Simulation, Taylor & Francis Journals, vol. 10(1), pages 69-77, February.
    6. Giulia Pedrielli & Andrea Matta & Arianna Alfieri & Mengyi Zhang, 2018. "Design and control of manufacturing systems: a discrete event optimisation methodology," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 543-564, January.
    7. Marius M. Solomon & Jacques Desrosiers, 1988. "Survey Paper---Time Window Constrained Routing and Scheduling Problems," Transportation Science, INFORMS, vol. 22(1), pages 1-13, February.
    8. Schultz, Michael & Evler, Jan & Asadi, Ehsan & Preis, Henning & Fricke, Hartmut & Wu, Cheng-Lung, 2020. "Future aircraft turnaround operations considering post-pandemic requirements," Journal of Air Transport Management, Elsevier, vol. 89(C).
    9. Evler, Jan & Asadi, Ehsan & Preis, Henning & Fricke, Hartmut, 2021. "Airline ground operations: Optimal schedule recovery with uncertain arrival times," Journal of Air Transport Management, Elsevier, vol. 92(C).
    10. Rabbani, M. & Heidari, R. & Yazdanparast, R., 2019. "A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 272(3), pages 945-961.
    11. Daniele Ferone & Aljoscha Gruler & Paola Festa & Angel A. Juan, 2019. "Enhancing and extending the classical GRASP framework with biased randomisation and simulation," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(8), pages 1362-1375, August.
    12. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
    13. Sergio Gonzalez-Martin & Angel A. Juan & Daniel Riera & Monica G. Elizondo & Juan J. Ramos, 2018. "A simheuristic algorithm for solving the arc routing problem with stochastic demands," Journal of Simulation, Taylor & Francis Journals, vol. 12(1), pages 53-66, January.
    14. Irawan, Chandra Ade & Eskandarpour, Majid & Ouelhadj, Djamila & Jones, Dylan, 2021. "Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm," European Journal of Operational Research, Elsevier, vol. 289(3), pages 912-926.
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