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Air cargo fleet routing and timetable setting with multiple on-time demands

Author

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  • Yan, Shangyao
  • Chen, Shin-Chin
  • Chen, Chia-Hung

Abstract

In this research we combine airport selection, fleet routing and timetable setting to develop an integrated scheduling model. The objective is to maximize operating profit, given the related operating constraints. The model is formulated as a mixed integer program that is characterized as NP-hard. We develop several heuristics, and incorporate the use a mathematical programming solver, to solve the problem. To evaluate the model and the solution algorithms, we perform a case study using real operating data from a major Taiwan airline. The results show that the model and the solution algorithms could be useful for actual operations.

Suggested Citation

  • Yan, Shangyao & Chen, Shin-Chin & Chen, Chia-Hung, 2006. "Air cargo fleet routing and timetable setting with multiple on-time demands," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(5), pages 409-430, September.
    • Handle: RePEc:eee:transe:v:42:y:2006:i:5:p:409-430
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    Citations

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    Cited by:

    1. Choi, Jong Hae, 2023. "The value of time and the impact on the air freight product portfolio - A study of the South Korean market 2017–2021," Journal of Air Transport Management, Elsevier, vol. 109(C).
    2. Lin, Dung-Ying & Chang, Yu-Ting, 2018. "Ship routing and freight assignment problem for liner shipping: Application to the Northern Sea Route planning problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 47-70.
    3. Jose L. Andrade-Pineda & Pedro L. Gonzalez-R & Jose M. Framinan, 2013. "A Decision-Making Tool for a Regional Network of Clinical Laboratories," Interfaces, INFORMS, vol. 43(4), pages 360-372, August.
    4. Nima Safaei & Dragan Banjevic & Andrew Jardine, 2011. "Workforce-constrained maintenance scheduling for military aircraft fleet: a case study," Annals of Operations Research, Springer, vol. 186(1), pages 295-316, June.
    5. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    6. Delgado, Felipe & Trincado, Ricardo & Pagnoncelli, Bernardo K., 2019. "A multistage stochastic programming model for the network air cargo allocation under capacity uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 292-307.
    7. Shangyao Yan & Chun-Ying Chen & Chuan-Che Wu, 2012. "Solution methods for the taxi pooling problem," Transportation, Springer, vol. 39(3), pages 723-748, May.
    8. 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).
    9. Shangyao Yan & Chun-Ying Chen, 2011. "An optimization model and a solution algorithm for the many-to-many car pooling problem," Annals of Operations Research, Springer, vol. 191(1), pages 37-71, November.
    10. 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.
    11. Jamili, Amin, 2017. "A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling, with consideration of fleet assignment problem," Journal of Air Transport Management, Elsevier, vol. 58(C), pages 21-30.
    12. Azadian, Farshid & Murat, Alper E. & Chinnam, Ratna Babu, 2012. "Dynamic routing of time-sensitive air cargo using real-time information," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 355-372.
    13. Vildan Özkır & Mahmud Sami Özgür, 2021. "Two-Phase Heuristic Algorithm for Integrated Airline Fleet Assignment and Routing Problem," Energies, MDPI, vol. 14(11), pages 1-10, June.
    14. 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).
    15. Yan, Shangyao & Lin, Jenn-Rong & Lai, Chun-Wei, 2013. "The planning and real-time adjustment of courier routing and scheduling under stochastic travel times and demands," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 53(C), pages 34-48.
    16. Chao, Ching-Cheng & Yu, Po-Cheng, 2013. "Quantitative evaluation model of air cargo competitiveness and comparative analysis of major Asia-Pacific airports," Transport Policy, Elsevier, vol. 30(C), pages 318-326.
    17. Delgado, Felipe & Sirhan, Cristóbal & Katscher, Mathias & Larrain, Homero, 2020. "Recovering from demand disruptions on an air cargo network," Journal of Air Transport Management, Elsevier, vol. 85(C).
    18. Ulrich Derigs & Stefan Friederichs & Simon Schäfer, 2009. "A New Approach for Air Cargo Network Planning," Transportation Science, INFORMS, vol. 43(3), pages 370-380, August.
    19. Moussawi-Haidar, Lama, 2014. "Optimal solution for a cargo revenue management problem with allotment and spot arrivals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 173-191.
    20. Carman K.M. Lee & Shuzhu Zhang & Kam K.H. Ng, 2019. "Design of An Integration Model for Air Cargo Transportation Network Design and Flight Route Selection," Sustainability, MDPI, vol. 11(19), pages 1-12, September.

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