دانلود مقاله ISI انگلیسی شماره 70503
عنوان فارسی مقاله

رویکرد سلسله مراتبی برای طراحی شبکه survivable

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
70503 2013 13 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Hierarchical approach for survivable network design
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : European Journal of Operational Research, Volume 225, Issue 2, 1 March 2013, Pages 223–235

کلمات کلیدی
الگوریتمهای فراابتکاری؛ طراحی شبکه Survivable؛ تجزیه و تحلیل اکتشافی؛ جستجوی ممنوع
پیش نمایش مقاله
پیش نمایش مقاله رویکرد سلسله مراتبی برای طراحی شبکه survivable

چکیده انگلیسی

A central design challenge facing network planners is how to select a cost-effective network configuration that can provide uninterrupted service despite edge failures. In this paper, we study the Survivable Network Design (SND) problem, a core model underlying the design of such resilient networks that incorporates complex cost and connectivity trade-offs. Given an undirected graph with specified edge costs and (integer) connectivity requirements between pairs of nodes, the SND problem seeks the minimum cost set of edges that interconnects each node pair with at least as many edge-disjoint paths as the connectivity requirement of the nodes. We develop a hierarchical approach for solving the problem that integrates ideas from decomposition, tabu search, randomization, and optimization. The approach decomposes the SND problem into two subproblems, Backbone design and Access design, and uses an iterative multi-stage method for solving the SND problem in a hierarchical fashion. Since both subproblems are NP-hard, we develop effective optimization-based tabu search strategies that balance intensification and diversification to identify near-optimal solutions. To initiate this method, we develop two heuristic procedures that can yield good starting points. We test the combined approach on large-scale SND instances, and empirically assess the quality of the solutions vis-à-vis optimal values or lower bounds. On average, our hierarchical solution approach generates solutions within 2.7% of optimality even for very large problems (that cannot be solved using exact methods), and our results demonstrate that the performance of the method is robust for a variety of problems with different size and connectivity characteristics.

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