Recovering from demand disruptions on an air cargo network
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DOI: 10.1016/j.jairtraman.2020.101799
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Cited by:
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- Bombelli, Alessandro, 2020. "Integrators' global networks: A topology analysis with insights into the effect of the COVID-19 pandemic," Journal of Transport Geography, Elsevier, vol. 87(C).
- Xiao, Fan & Guo, Siqi & Huang, Lin & Huang, Lei & Liang, Zhe, 2022. "Integrated aircraft tail assignment and cargo routing problem with through cargo consideration," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 328-351.
- Brown, Richard S. & Kline, William A., 2020. "Exogenous shocks and managerial preparedness: A study of U.S. airlines’ environmental scanning before the onset of the COVID-19 pandemic," Journal of Air Transport Management, Elsevier, vol. 89(C).
- Huang, Lei & Xiao, Fan & Zhou, Jing & Duan, Zhenya & Zhang, Hua & Liang, Zhe, 2023. "A machine learning based column-and-row generation approach for integrated air cargo recovery problem," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
- Delgado, Felipe & Mora, Julio, 2021. "A matheuristic approach to the air-cargo recovery problem under demand disruption," Journal of Air Transport Management, Elsevier, vol. 90(C).
- Tanrıverdi, Gökhan & Bakır, Mahmut & Merkert, Rico, 2020. "What can we learn from the JATM literature for the future of aviation post Covid-19? - A bibliometric and visualization analysis," Journal of Air Transport Management, Elsevier, vol. 89(C).
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Keywords
Air cargo schedule recovery; Airline schedule recovery; Disruption management; Air cargo rescheduling;All these keywords.
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