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Scheduling de-icing vehicles within airport logistics: a heuristic algorithm and performance evaluation

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  • A Norin

    (1] Linköping University, Norrköping, Sweden[2] Swedavia Teknik, Swedish Airports, Norrköping, Sweden)

  • D Yuan

    (Linköping University, Norrköping, Sweden)

  • T A Granberg

    (Linköping University, Norrköping, Sweden)

  • P V&aauml;rbrand

    (Linköping University, Norrköping, Sweden)

Abstract

Most delays in the air transport occur at the airport. A particular reason is the complexity of managing the large number of supporting flows in airport logistics. We consider the optimisation problem of scheduling de-icing vehicles that is one of the key supporting logistic flows in the turn-around process of aircraft. The objective is to minimise the delay of flights due to de-icing, and the travel distance of the de-icing vehicles. We study the complexity of the problem, and develop a solution algorithm using greedy randomised adaptive search. A case study of real-life data from Stockholm Arlanda Airport shows that optimised schedule leads to significantly better performance in comparison to intuitive and simple scheduling strategies. The benefit of optimisation in reducing the waiting time for de-icing is further demonstrated via dynamic simulations.

Suggested Citation

  • A Norin & D Yuan & T A Granberg & P V&aauml;rbrand, 2012. "Scheduling de-icing vehicles within airport logistics: a heuristic algorithm and performance evaluation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(8), pages 1116-1125, August.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:8:p:1116-1125
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    Citations

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    Cited by:

    1. Ivan Kovynyov & Ralf Mikut, 2019. "Digital technologies in airport ground operations," Netnomics, Springer, vol. 20(1), pages 1-30, April.
    2. Xue Han & Peixin Zhao & Qingchun Meng & Shengnan Yin & Di Wan, 2020. "Optimal scheduling of airport ferry vehicles based on capacity network," Annals of Operations Research, Springer, vol. 295(1), pages 163-182, December.
    3. Zhang, Zhenzhen & Che, Yuxin & Liang, Zhe, 2024. "Split-demand multi-trip vehicle routing problem with simultaneous pickup and delivery in airport baggage transit," European Journal of Operational Research, Elsevier, vol. 312(3), pages 996-1010.
    4. 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.
    5. Han, Xue & Zhao, Peixin & Kong, Dexin, 2023. "Two-stage optimization of airport ferry service delay considering flight uncertainty," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1103-1116.
    6. 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).
    7. Bao, Dan-Wen & Zhou, Jia-Yi & Zhang, Zi-Qian & Chen, Zhuo & Kang, Di, 2023. "Mixed fleet scheduling method for airport ground service vehicles under the trend of electrification," Journal of Air Transport Management, Elsevier, vol. 108(C).
    8. 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).

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