2006
DOI: 10.1007/0-387-29234-9_9
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Compact Models for Hop-Constrained Node Survivable Network Design: An Application to MPLS

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Cited by 13 publications
(24 citation statements)
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“…We will consider two building blocks, one corresponding to the design of the primary path, and the other to the set of the backup edges. While models for designing the primary path can be straightforwardly taken from the literature (see, e.g., [5,6] and [17] for a more recent article), this is not true for models used to determine the optimal set of backup edges. In Section 3.2, we will provide three characterizations of valid sets of backup edges.…”
Section: Ilp Formulationsmentioning
confidence: 99%
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“…We will consider two building blocks, one corresponding to the design of the primary path, and the other to the set of the backup edges. While models for designing the primary path can be straightforwardly taken from the literature (see, e.g., [5,6] and [17] for a more recent article), this is not true for models used to determine the optimal set of backup edges. In Section 3.2, we will provide three characterizations of valid sets of backup edges.…”
Section: Ilp Formulationsmentioning
confidence: 99%
“…Relating the LP bounds of the two formulations We start by pointing out that in several situations, the aggregated model produces the same LP bound as the disaggregated model, see for instance [6], in which case it is clearly preferable to the disaggregated one. In fact, it is also with such an aggregated model that the good results reported in Botton et al [3] are obtained.…”
Section: -(20) This Formulation Uses Integer Variableszmentioning
confidence: 99%
“…Following Gouveia et al (2006) we present next a hop-indexed formulation for the problem, which involves three sets of variables. Let N i (i ∈ V \ S) be binary variables indicating whether a core LSR is put in operation on node i, let u e (e ∈ E ) be integer variables representing the number of lightpaths installed on edge e, and let w hpq ij ( p, q ∈ S; {i, j} ∈ E ; h = 1, ..., H)(the variables with i = q and j = q are not defined and, also, with j = p) be binary variables indicating whether a ( p, q) − H − path traverses edge {i, j} in the direction from i to j and in position h. Note that, as explained in Gouveia et al (2003Gouveia et al ( , 2006, when i = j = q we have "loop" variables w hpq qq (h = 2, ..., H) which model situations when a path (or paths) from node p to node q contains fewer than H arcs (that is, w hpq jq = 1 for some j ∈ V \ {q} and 1 ≤ h ≤ H − 1).…”
Section: Modelmentioning
confidence: 99%
“…The other formulation is an aggregated version of the previous one and, for each pair of demand nodes, uses a single hop-indexed flow system for the two paths. The main result of Gouveia et al (2006) states that the linear programming relaxation of the two formulations are equivalent. That is, in this case, aggregation does not lead to a formulation with a weaker linear programming relaxation.…”
Section: Introductionmentioning
confidence: 98%
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