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Location models for airline hubs behaving as M/D/c queues

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Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value $\alpha$. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.

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  • Vladimir Marianov & Daniel Serra, 2000. "Location models for airline hubs behaving as M/D/c queues," Economics Working Papers 453, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:453
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    Cited by:

    1. Jayaswal, Sachin & Vidyarthi, Navneet, 2013. "Capacitated Multiple Allocation Hub Location with Service Level Constraints for Multiple Consignment Classes," IIMA Working Papers WP2013-11-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    2. Ishfaq, Rafay & Sox, Charles R., 2012. "Design of intermodal logistics networks with hub delays," European Journal of Operational Research, Elsevier, vol. 220(3), pages 629-641.
    3. V Marianov & T B Boffey & R D Galvão, 2009. "Optimal location of multi-server congestible facilities operating as M/E r /m/N queues," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 674-684, May.
    4. Caccavale, Maria Virginia & Iovanella, Antonio & Lancia, Carlo & Lulli, Guglielmo & Scoppola, Benedetto, 2014. "A model of inbound air traffic: The application to Heathrow airport," Journal of Air Transport Management, Elsevier, vol. 34(C), pages 116-122.
    5. Edward Hult & Houyuan Jiang & Daniel Ralph, 2014. "Exact computational approaches to a stochastic uncapacitated single allocation p-hub center problem," Computational Optimization and Applications, Springer, vol. 59(1), pages 185-200, October.
    6. Vahdani, Behnam & Tavakkoli-Moghaddam, Reza & Modarres, Mohammad & Baboli, Armand, 2012. "Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1152-1168.
    7. G. Guadagni & S. Ndreca & B. Scoppola, 2011. "Queueing systems with pre-scheduled random arrivals," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 73(1), pages 1-18, February.
    8. Alumur, Sibel A. & Nickel, Stefan & Saldanha-da-Gama, Francisco, 2012. "Hub location under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 529-543.
    9. Zhongfeng Qin & Yuan Gao, 2017. "Uncapacitated $$p$$ p -hub location problem with fixed costs and uncertain flows," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 705-716, March.
    10. Jiyoung Choi & Chungmok Lee & Sungsoo Park, 2018. "Dantzig–Wolfe decomposition approach to the vehicle assignment problem with demand uncertainty in a hybrid hub-and-spoke network," Annals of Operations Research, Springer, vol. 264(1), pages 57-87, May.
    11. B Boffey & R D Galvão & V Marianov, 2010. "Location of single-server immobile facilities subject to a loss constraint," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 987-999, June.
    12. Contreras, Ivan & Cordeau, Jean-François & Laporte, Gilbert, 2011. "Stochastic uncapacitated hub location," European Journal of Operational Research, Elsevier, vol. 212(3), pages 518-528, August.

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    More about this item

    Keywords

    Hub location; congestion; tabu-search;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R53 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Public Facility Location Analysis; Public Investment and Capital Stock

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