P-hub protection models for survivable hub network design
AbstractThe design of survivable networks has been a significant issue in network-based infrastructure in transportation, electric power systems, and telecommunications. In telecommunications networks, hubs and backbones are the most critical assets to be protected from any network failure because many network flows use these facilities, resulting in an intensive concentration of flows at these facilities. This paper addresses a series of new hub and spoke network models as survivable network designs, which are termed p-hub protection models (PHPRO). The PHPRO aim to build networks that maximize the total potential interacting traffic over a set of origin–destination nodes based on different routing assumptions, including multiple assignments and back-up hub routes with distance restrictions. Empirical analyses are presented using telecommunication networks in the United States, and the vulnerabilities of networks based on possible disruption scenarios are examined. The results reveal that PROBA, the model with a back-up routing scheme, considerably enhances the network resilience and even the network performance, indicating that the model is a candidate for a strong survivable hub network design. An extension, PROBA-D, also shows that applying a distance restriction can be strategically used for designing back-up hub routes if a network can trade off between network performance and network cost, which results from the reduced length of back-up routings. Copyright Springer-Verlag 2012
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Bibliographic InfoArticle provided by Springer in its journal Journal of Geographical Systems.
Volume (Year): 14 (2012)
Issue (Month): 4 (October)
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Web page: http://www.springerlink.com/link.asp?id=103079
Hub location; Hub network design; Survivability; C60; C61;
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- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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