IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v54y2013i3p619-643.html
   My bibliography  Save this article

The robust network loading problem with dynamic routing

Author

Listed:
  • Sara Mattia

Abstract

In this paper the Robust Network Loading problem with splittable flows and dynamic routing under polyhedral uncertainty for the demands is considered. Polyhedral results for the capacity formulation of the problem are given. The first exact approach for solving the problem is presented. A branch-and-cut algorithm based on the proposed capacity formulation is developed. Computational results using the hose polyhedron to model the demand uncertainty are discussed. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Sara Mattia, 2013. "The robust network loading problem with dynamic routing," Computational Optimization and Applications, Springer, vol. 54(3), pages 619-643, April.
    • Handle: RePEc:spr:coopap:v:54:y:2013:i:3:p:619-643
    DOI: 10.1007/s10589-012-9500-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-012-9500-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-012-9500-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alper Atamtürk & Muhong Zhang, 2007. "Two-Stage Robust Network Flow and Design Under Demand Uncertainty," Operations Research, INFORMS, vol. 55(4), pages 662-673, August.
    2. Gianpaolo Oriolo, 2008. "Domination Between Traffic Matrices," Mathematics of Operations Research, INFORMS, vol. 33(1), pages 91-96, February.
    3. Sara Mattia, 2012. "Solving survivable two-layer network design problems by metric inequalities," Computational Optimization and Applications, Springer, vol. 51(2), pages 809-834, March.
    4. Thomas L. Magnanti & Prakash Mirchandani & Rita Vachani, 1995. "Modeling and Solving the Two-Facility Capacitated Network Loading Problem," Operations Research, INFORMS, vol. 43(1), pages 142-157, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Josette Ayoub & Michael Poss, 2016. "Decomposition for adjustable robust linear optimization subject to uncertainty polytope," Computational Management Science, Springer, vol. 13(2), pages 219-239, April.
    2. Artur Alves Pessoa & Michael Poss, 2015. "Robust Network Design with Uncertain Outsourcing Cost," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 507-524, August.
    3. Álvarez-Miranda, Eduardo & Cacchiani, Valentina & Lodi, Andrea & Parriani, Tiziano & Schmidt, Daniel R., 2014. "Single-commodity robust network design problem: Complexity, instances and heuristic solutions," European Journal of Operational Research, Elsevier, vol. 238(3), pages 711-723.
    4. Dimitris Bertsimas & Ebrahim Nasrabadi & Sebastian Stiller, 2013. "Robust and Adaptive Network Flows," Operations Research, INFORMS, vol. 61(5), pages 1218-1242, October.
    5. Sara Mattia & Michael Poss, 2018. "A comparison of different routing schemes for the robust network loading problem: polyhedral results and computation," Computational Optimization and Applications, Springer, vol. 69(3), pages 753-800, April.
    6. Siqian Shen & Mingdi You & Yintai Ma, 2017. "Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(2), pages 154-173, March.
    7. Christina Büsing & Arie M. C. A. Koster & Sabrina Schmitz, 2022. "Robust minimum cost flow problem under consistent flow constraints," Annals of Operations Research, Springer, vol. 312(2), pages 691-722, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yogesh Agarwal, 2013. "Design of Survivable Networks Using Three- and Four-Partition Facets," Operations Research, INFORMS, vol. 61(1), pages 199-213, February.
    2. Agarwal, Y.K. & Venkateshan, Prahalad, 2014. "Survivable network design with shared-protection routing," European Journal of Operational Research, Elsevier, vol. 238(3), pages 836-845.
    3. Ayşegül Altın & Hande Yaman & Mustafa Ç. Pınar, 2011. "The Robust Network Loading Problem Under Hose Demand Uncertainty: Formulation, Polyhedral Analysis, and Computations," INFORMS Journal on Computing, INFORMS, vol. 23(1), pages 75-89, February.
    4. David Simchi-Levi & Nikolaos Trichakis & Peter Yun Zhang, 2019. "Designing Response Supply Chain Against Bioattacks," Operations Research, INFORMS, vol. 67(5), pages 1246-1268, September.
    5. C S Sung & S H Song, 2003. "Integrated service network design for a cross-docking supply chain network," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(12), pages 1283-1295, December.
    6. Wang, Zujian & Qi, Mingyao, 2019. "Service network design considering multiple types of services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 1-14.
    7. Sara Mattia & Michael Poss, 2018. "A comparison of different routing schemes for the robust network loading problem: polyhedral results and computation," Computational Optimization and Applications, Springer, vol. 69(3), pages 753-800, April.
    8. Sun, Hao & Yang, Jun & Yang, Chao, 2019. "A robust optimization approach to multi-interval location-inventory and recharging planning for electric vehicles," Omega, Elsevier, vol. 86(C), pages 59-75.
    9. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    10. Liang Chen & Wei-Kun Chen & Mu-Ming Yang & Yu-Hong Dai, 2021. "An exact separation algorithm for unsplittable flow capacitated network design arc-set polyhedron," Journal of Global Optimization, Springer, vol. 81(3), pages 659-689, November.
    11. Se-Hyeok Choi & Akhtar Hussain & Hak-Man Kim, 2018. "Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-16, October.
    12. Fernando Ordóñez & Nicolás E. Stier-Moses, 2010. "Wardrop Equilibria with Risk-Averse Users," Transportation Science, INFORMS, vol. 44(1), pages 63-86, February.
    13. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2021. "Robust facility location under demand uncertainty and facility disruptions," Omega, Elsevier, vol. 103(C).
    14. Crainic, Teodor Gabriel, 2000. "Service network design in freight transportation," European Journal of Operational Research, Elsevier, vol. 122(2), pages 272-288, April.
    15. Luís Gouveia & Pedro Patrício & Amaro Sousa, 2008. "Hop-Constrained Node Survivable Network Design: An Application to MPLS over WDM," Networks and Spatial Economics, Springer, vol. 8(1), pages 3-21, March.
    16. Jia, Shuai & Li, Chung-Lun & Meng, Qiang, 2024. "The dry dock scheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).
    17. Yogesh K. Agarwal, 2002. "Design of Capacitated Multicommodity Networks with Multiple Facilities," Operations Research, INFORMS, vol. 50(2), pages 333-344, April.
    18. Lebing Wang & Jian Gang Jin & Gleb Sibul & Yi Wei, 2023. "Designing Metro Network Expansion: Deterministic and Robust Optimization Models," Networks and Spatial Economics, Springer, vol. 23(1), pages 317-347, March.
    19. Oğuz Solyalı & Jean-François Cordeau & Gilbert Laporte, 2016. "The Impact of Modeling on Robust Inventory Management Under Demand Uncertainty," Management Science, INFORMS, vol. 62(4), pages 1188-1201, April.
    20. Christoph Buchheim & Jannis Kurtz, 2018. "Robust combinatorial optimization under convex and discrete cost uncertainty," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 211-238, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:54:y:2013:i:3:p:619-643. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.