Survivable networks, linear programming relaxations and the parsimonious property
Mathematical Programming: Series A and B
A dual-based algorithm for multi-level network design
Management Science
A Cutting Plane Algorithm for Multicommodity Survivable Network Design Problems
INFORMS Journal on Computing
Spare-Capacity Assignment For Line Restoration Using a Single-Facility Type
Operations Research
Two Edge-Disjoint Hop-Constrained Paths and Polyhedra
SIAM Journal on Discrete Mathematics
On the structure and complexity of the 2-connected Steiner network problem in the plane
Operations Research Letters
Two-connected Steiner networks: structural properties
Operations Research Letters
Design of Survivable Networks Using Three-and Four-Partition Facets
Operations Research
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Disruptions in infrastructure networks to transport material, energy, and information can have serious economic, and even catastrophic, consequences. Since these networks require enormous investments, network service providers emphasize both survivability and cost effectiveness in their topological design decisions. This paper addresses the survivable network design problem, a core model incorporating the cost and redundancy trade-offs facing network planners. Using a novel connectivity upgrade strategy, we develop several families of inequalities to strengthen a multicommodity flow-based formulation for the problem, and show that some of these inequalities are facet defining. By increasing the linear programming lower bound, the valid inequalities not only lead to better performance guarantees for heuristic solutions, but also accelerate exact and approximate solution methods. We also consider a heuristic strategy that sequentially rounds the fractional values, starting with the linear programming solution to our strong model. Extensive computational tests confirm that the valid inequalities, added via a cutting plane algorithm, and the heuristic procedure are very effective, and their performance is robust to changes in the network dimensions and connectivity structure. Our solution approach generates tight lower and upper bounds with average gaps that are less than 1.2% for various problem sizes and connectivity requirements.