An efficient implementation of a scaling minimum-cost flow algorithm
Journal of Algorithms
Designing least-cost nonblocking broadband networks
Journal of Algorithms
Minimum cost capacity installation for multicommodity network flows
Mathematical Programming: Series A and B - Special issue on computational integer programming
A flexible model for resource management in virtual private networks
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Simpler and better approximation algorithms for network design
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Provisioning virtual private networks under traffic uncertainty
Networks - Special Issue on Multicommodity Flows and Network Design
New Approaches for Virtual Private Network Design
SIAM Journal on Computing
The VPN Problem with Concave Costs
SIAM Journal on Discrete Mathematics
An exact algorithm for robust network design
INOC'11 Proceedings of the 5th international conference on Network optimization
Operations Research Letters
Metric inequalities and the Network Loading Problem
Discrete Optimization
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We consider a robust network design problem: optimum integral capacities need to be installed in a network such that supplies and demands in each of the explicitly known traffic scenarios can be satisfied by a single-commodity flow. In Buchheim et al. (LNCS 6701, 7---17 (2011)), an integer-programming (IP) formulation of polynomial size was given that uses both flow and capacity variables. We introduce an IP formulation that only uses capacity variables and exponentially many, but polynomial time separable constraints. We discuss the advantages of the latter formulation for branch-and-cut implemenations and evaluate preliminary computational results for the root bounds. We define a class of instances that is difficult for IP-based approaches. Finally, we design and implement a heuristic solution approach based on the exploration of large neighborhoods of carefully selected size and evaluate it on the difficult instances. The results are encouraging, with a good understanding of the trade-off between solution quality and neighborhood size.