IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v210y2011i1p10-26.html
   My bibliography  Save this article

Survivable network design with demand uncertainty

Author

Listed:
  • Terblanche, S.E.
  • Wessäly, R.
  • Hattingh, J.M.

Abstract

The objective in designing a communications network is to find the most cost efficient network design that specifies hardware devices to be installed, the type of transmission links to be installed, and the routing strategy to be followed. In this paper algorithmic ideas are presented for improving tractability in solving the survivable network design problem by taking into account uncertainty in the traffic requirements. Strategies for improving separation of metric inequalities are presented and an iterative approach for obtaining solutions, that significantly reduces computing times, is introduced. Computational results are provided based on data collected from an operational network.

Suggested Citation

  • Terblanche, S.E. & Wessäly, R. & Hattingh, J.M., 2011. "Survivable network design with demand uncertainty," European Journal of Operational Research, Elsevier, vol. 210(1), pages 10-26, April.
    • Handle: RePEc:eee:ejores:v:210:y:2011:i:1:p:10-26
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(10)00639-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Lisser, A. & Ouorou, A. & Vial, J.-P. & Gondzio, J., 1999. "Capacity Planning under Uncertain Demand in Telecommunication Networks," Papers 99.13, Ecole des Hautes Etudes Commerciales, Universite de Geneve-.
    2. Geir Dahl & Mechthild Stoer, 1998. "A Cutting Plane Algorithm for Multicommodity Survivable Network Design Problems," INFORMS Journal on Computing, INFORMS, vol. 10(1), pages 1-11, February.
    3. Chari, Kaushal & Dutta, Amitava, 1993. "Design of private backbone networks -- II: time varying grouped traffic," European Journal of Operational Research, Elsevier, vol. 67(3), pages 443-452, June.
    4. Amiri, Ali & Pirkul, Hasan, 1999. "Routing and capacity assignment in backbone communication networks under time varying traffic conditions," European Journal of Operational Research, Elsevier, vol. 117(1), pages 15-29, August.
    5. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    6. Morten Riis & Kim Allan Andersen, 2002. "Capacitated Network Design with Uncertain Demand," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 247-260, August.
    7. Chari, Kaushal & Dutta, Amitava, 1993. "Design of private backbone networks -- I: time varying traffic," European Journal of Operational Research, Elsevier, vol. 67(3), pages 428-442, June.
    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. Le Thi Khanh Hien & Melvyn Sim & Huan Xu, 2020. "Mitigating Interdiction Risk with Fortification," Operations Research, INFORMS, vol. 68(2), pages 348-362, March.
    2. Balakrishnan, Anantaram & Banciu, Mihai & Glowacka, Karolina & Mirchandani, Prakash, 2013. "Hierarchical approach for survivable network design," European Journal of Operational Research, Elsevier, vol. 225(2), pages 223-235.

    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. Garcia, Bruno-Laurent & Mahey, Philippe & LeBlanc, Larry J., 1998. "Iterative improvement methods for a multiperiod network design problem," European Journal of Operational Research, Elsevier, vol. 110(1), pages 150-165, October.
    2. M Riis & A J V Skriver & S F Møller, 2005. "Internet protocol network design with uncertain demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1184-1195, October.
    3. Michael Freimer & Jeffrey Linderoth & Douglas Thomas, 2012. "The impact of sampling methods on bias and variance in stochastic linear programs," Computational Optimization and Applications, Springer, vol. 51(1), pages 51-75, January.
    4. Aghayi, Nazila & Maleki, Bentolhoda, 2016. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on bank industry," Energy, Elsevier, vol. 112(C), pages 376-387.
    5. J. F. F. Almeida & S. V. Conceição & L. R. Pinto & B. R. P. Oliveira & L. F. Rodrigues, 2022. "Optimal sales and operations planning for integrated steel industries," Annals of Operations Research, Springer, vol. 315(2), pages 773-790, August.
    6. Kanudia, Amit & Shukla, PR, 1998. "Modelling of Uncertainties and Price Elastic Demands in Energy-environment Planning for India," Omega, Elsevier, vol. 26(3), pages 409-423, June.
    7. Arie M. C. A. Koster & Michael Poss, 2018. "Special issue on: robust combinatorial optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 207-209, September.
    8. Rashed Khanjani-Shiraz & Ali Babapour-Azar & Zohreh Hosseini-Noudeh & Panos M. Pardalos, 2022. "Distributionally robust maximum probability shortest path problem," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 140-167, January.
    9. Walid Ben-Ameur & Adam Ouorou & Guanglei Wang & Mateusz Żotkiewicz, 2018. "Multipolar robust optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 395-434, December.
    10. Lambert, David K. & McCarl, Bruce A. & He, Quifen & Kaylen, Michael S. & Rosenthal, Wesley & Chang, Ching-Cheng & Nayda, W.I., 1995. "Uncertain Yields In Sectoral Welfare Analysis: An Application To Global Warming," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 27(2), pages 1-14, December.
    11. Hsien-Chung Wu, 2019. "Numerical Method for Solving the Robust Continuous-Time Linear Programming Problems," Mathematics, MDPI, vol. 7(5), pages 1-50, May.
    12. Ketabchi, Saeed & Behboodi-Kahoo, Malihe, 2015. "Augmented Lagrangian method within L-shaped method for stochastic linear programs," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 12-20.
    13. Ceren Tuncer Şakar & Murat Köksalan, 2013. "A stochastic programming approach to multicriteria portfolio optimization," Journal of Global Optimization, Springer, vol. 57(2), pages 299-314, October.
    14. Alexander Engau, 2017. "Proper Efficiency and Tradeoffs in Multiple Criteria and Stochastic Optimization," Mathematics of Operations Research, INFORMS, vol. 42(1), pages 119-134, January.
    15. Kallio, Markku & Halme, Merja & Dehghan Hardoroudi, Nasim & Aspara, Jaakko, 2022. "Transparent structured products for retail investors," European Journal of Operational Research, Elsevier, vol. 302(2), pages 752-767.
    16. Yogesh Agarwal, 2013. "Design of Survivable Networks Using Three- and Four-Partition Facets," Operations Research, INFORMS, vol. 61(1), pages 199-213, February.
    17. Zuo, Qiting & Wu, Qingsong & Yu, Lei & Li, Yongping & Fan, Yurui, 2021. "Optimization of uncertain agricultural management considering the framework of water, energy and food," Agricultural Water Management, Elsevier, vol. 253(C).
    18. Guanglei Wang & Hassan Hijazi, 2018. "Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches," Computational Optimization and Applications, Springer, vol. 71(2), pages 553-608, November.
    19. Pengyu Qian & Zizhuo Wang & Zaiwen Wen, 2015. "A Composite Risk Measure Framework for Decision Making under Uncertainty," Papers 1501.01126, arXiv.org.
    20. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.

    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:eee:ejores:v:210:y:2011:i:1:p:10-26. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.