The hop-limit approach for spare-capacity assignment in survivable networks
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Optimal capacity placement for path restoration in STM or ATM mesh-survivable networks
IEEE/ACM Transactions on Networking (TON)
An iterative algorithm for delay-constrained minimum-cost multicasting
IEEE/ACM Transactions on Networking (TON)
Optical networks: a practical perspective
Optical networks: a practical perspective
Designing Hierarchical Survivable Networks
Operations Research
An Efficient Decomposition Algorithm to Optimize Spare Capacity in a Telecommunications Network
INFORMS Journal on Computing
Mesh-based Survivable Transport Networks: Options and Strategies for Optical, MPLS, SONET and ATM Networking
Base Station Location and Service Assignments in W--CDMA Networks
INFORMS Journal on Computing
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This study focuses on the mathematical modeling of the optimal spare capacity assignment problem for the links of a telecommunications network. Given a network topology, a point-to-point demand matrix with demand routings, and the permissible values for link capacity, we optimize the assignment of spare capacity to the links of the network in order for it to survive single link failures. The modular spare capacity allocation problem is formulated as a mixed-integer program which is computationally expensive for all but small problem instances. To be able to solve large practical problem instances, we strengthen the continuous relaxation by including additional constraints related to cuts in the network topology graph. Our solution approach is to decompose the problem into a pair of smaller problems for which optimal solutions can be obtained within a realistic time constraint. Combining the solutions for the subproblems results in a feasible solution for the original problem. We analyze the efficiency of cut-generating techniques used to derive additional constraints, and we empirically investigate the performance of the decomposition approach. The numerical results indicate that the combination of additional constraints and the decomposition algorithm improves solution times when compared to solving the original mixed-integer program. The solution time improvement using the proposed heuristics can be as high as an order of magnitude for small problem instances, while large practical problem instances can be solved in less than half the time.