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Optimizing Restoration Capacity in the AT&T Network

Author

Listed:
  • Ken Ambs

    (AT&T Network Services, 900 Route 202/206 North, Bedminster, New Jersey 07921)

  • Sebastian Cwilich

    (AT&T Labs, 200 Laurel Avenue, Middletown, New Jersey 07748)

  • Mei Deng

    (AT&T Labs)

  • David J. Houck

    (Bell Laboratories, Lucent Technologies, 101 Crawfords Corner Road, Holmdel, New Jersey 07733)

  • David F. Lynch

    (AT&T Labs)

  • Dicky Yan

    (AT&T Network Services)

Abstract

To ensure high network reliability, AT&T employs two basic approaches: preventing failures and responding quickly when failures occur. For AT&T to quickly reroute traffic in the event of a network failure, the network must contain sufficient restoration capacity to carry the displaced demand. A team of AT&T OR experts, network planners, and managers developed a method for determining the appropriate quantity and location of restoration capacity required to restore the demand during any single link failure. The approach centers on a linear programming model to minimize the cost of the restoration network and uses column generation to generate new restoration paths as needed. In about 10 months, the team converted the methodology into a tool to optimize the allocation of restoration capacity. This tool was then extended to plan for the recovery of a switching-center disaster and to reoptimize the entire restoration network. It has contributed to AT&T's achieving high-quality service, while saving valuable resources. It resulted in hundreds of millions of dollars in cost savings and increased revenues.

Suggested Citation

  • Ken Ambs & Sebastian Cwilich & Mei Deng & David J. Houck & David F. Lynch & Dicky Yan, 2000. "Optimizing Restoration Capacity in the AT&T Network," Interfaces, INFORMS, vol. 30(1), pages 26-44, February.
  • Handle: RePEc:inm:orinte:v:30:y:2000:i:1:p:26-44
    DOI: 10.1287/inte.30.1.26.11621
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    Citations

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    Cited by:

    1. Sahebjamnia, Navid & Torabi, S. Ali & Mansouri, S. Afshin, 2018. "Building organizational resilience in the face of multiple disruptions," International Journal of Production Economics, Elsevier, vol. 197(C), pages 63-83.
    2. Joe Naoum-Sawaya & Bissan Ghaddar, 2017. "Cutting plane approach for the maximum flow interdiction problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1553-1569, December.
    3. Torabi, S.A. & Mansouri, S.A., 2015. "Integrated business continuity and disaster recovery planning: Towards organizational resilienceAuthor-Name: Sahebjamnia, N," European Journal of Operational Research, Elsevier, vol. 242(1), pages 261-273.
    4. Timothy Matisziw & Alan Murray & Tony Grubesic, 2010. "Strategic Network Restoration," Networks and Spatial Economics, Springer, vol. 10(3), pages 345-361, September.
    5. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.

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