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A Scenario Aggregation–Based Approach for Determining a Robust Airline Fleet Composition for Dynamic Capacity Allocation

Author

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  • Ovidiu Listes

    (Paragon Decision Technology B.V., P.O. Box 3277, 2001 DG Haarlem, The Netherlands)

  • Rommert Dekker

    (Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands)

Abstract

Recently, airlines and aircraft manufacturers have realized the benefits of the emerging concept of dynamic capacity allocation, and have initiated advanced decision support systems to assist them in this respect. Strategic airline fleet planning is one of the major issues addressed through such systems. We present background research connected with the dynamic allocation concept, which accounts explicitly for the stochastic nature of passenger demand in the fleet composition problem. We address this problem through a scenario aggregation–based approach and present results on representative case studies based on realistic data. Our investigations establish clear benefits of a stochastic approach as compared with deterministic formulations, as well as its implementation feasibility using state-of-the-art optimization software.

Suggested Citation

  • Ovidiu Listes & Rommert Dekker, 2005. "A Scenario Aggregation–Based Approach for Determining a Robust Airline Fleet Composition for Dynamic Capacity Allocation," Transportation Science, INFORMS, vol. 39(3), pages 367-382, August.
    • Handle: RePEc:inm:ortrsc:v:39:y:2005:i:3:p:367-382
    DOI: 10.1287/trsc.1040.0097
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    References listed on IDEAS

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    9. Lay Eng Teoh & Hooi Ling Khoo, 2016. "Fleet Planning Decision-Making: Two-Stage Optimization with Slot Purchase," Journal of Optimization, Hindawi, vol. 2016, pages 1-12, June.
    10. Pilla, Venkata L. & Rosenberger, Jay M. & Chen, Victoria & Engsuwan, Narakorn & Siddappa, Sheela, 2012. "A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model," European Journal of Operational Research, Elsevier, vol. 216(1), pages 162-171.
    11. Şafak, Özge & Çavuş, Özlem & Aktürk, M. Seli̇m, 2022. "A two-stage decision dependent stochastic approach for airline flight network expansion," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 78-101.
    12. Kenan, Nabil & Diabat, Ali & Jebali, Aida, 2018. "Codeshare agreements in the integrated aircraft routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 272-295.
    13. Birolini, Sebastian & Jacquillat, Alexandre & Cattaneo, Mattia & Antunes, António Pais, 2021. "Airline Network Planning: Mixed-integer non-convex optimization with demand–supply interactions," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 100-124.
    14. Şafak, Özge & Çavuş, Özlem & Selim Aktürk, M., 2018. "Multi-stage airline scheduling problem with stochastic passenger demand and non-cruise times," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 39-67.
    15. Ye Wang & Jiafu Tang, 2022. "Optimized skill configuration for the seru production system under an uncertain demand," Annals of Operations Research, Springer, vol. 316(1), pages 445-465, September.
    16. Geursen, Izaak L. & Santos, Bruno F. & Yorke-Smith, Neil, 2023. "Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning," Journal of Air Transport Management, Elsevier, vol. 109(C).
    17. Hanif D. Sherali & Xiaomei Zhu, 2008. "Two-Stage Fleet Assignment Model Considering Stochastic Passenger Demands," Operations Research, INFORMS, vol. 56(2), pages 383-399, April.
    18. Hu, Shaolong & Han, Chuanfeng & Dong, Zhijie Sasha & Meng, Lingpeng, 2019. "A multi-stage stochastic programming model for relief distribution considering the state of road network," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 64-87.
    19. Sa, Constantijn A.A. & Santos, Bruno F. & Clarke, John-Paul B., 2020. "Portfolio-based airline fleet planning under stochastic demand," Omega, Elsevier, vol. 97(C).
    20. Kiracı, Kasım & Akan, Ercan, 2020. "Aircraft selection by applying AHP and TOPSIS in interval type-2 fuzzy sets," Journal of Air Transport Management, Elsevier, vol. 89(C).
    21. Adil Baykasoğlu & Nurhan Dudaklı & Kemal Subulan & A. Serdar Taşan, 2022. "An integrated fleet planning model with empty vehicle repositioning for an intermodal transportation system," Operational Research, Springer, vol. 22(3), pages 2063-2098, July.
    22. Serhat Gul & Brian T. Denton & John W. Fowler, 2015. "A Progressive Hedging Approach for Surgery Planning Under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 755-772, November.
    23. Qiang Meng & Tingsong Wang & Shuaian Wang, 2015. "Multi-period liner ship fleet planning with dependent uncertain container shipment demand," Maritime Policy & Management, Taylor & Francis Journals, vol. 42(1), pages 43-67, January.
    24. Dožić, Slavica & Kalić, Milica, 2015. "Three-stage airline fleet planning model," Journal of Air Transport Management, Elsevier, vol. 46(C), pages 30-39.

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