Clustering for the design of SONET rings in interoffice telecommunications
Management Science
SIAM Journal on Discrete Mathematics
Scalable WDM access network architecture based on photonic slot routing
IEEE/ACM Transactions on Networking (TON)
Between Min Cut and Graph Bisection
MFCS '93 Proceedings of the 18th International Symposium on Mathematical Foundations of Computer Science
Optimal Placement of Add/Drop Multiplexers: Heuristic and Exact Algorithms
Operations Research
Exact solution of the SONET Ring Loading Problem
Operations Research Letters
Comparing Metaheuristic Algorithms for Sonet Network Design Problems
Journal of Heuristics
A ring-mesh topology design problem for optical transport networks
Journal of Heuristics
Computers and Industrial Engineering
Solving ring loading problems using bio-inspired algorithms
Journal of Network and Computer Applications
Improved formulations for the ring spur assignment problem
INOC'11 Proceedings of the 5th international conference on Network optimization
Using a hybrid honey bees mating optimisation algorithm for solving SONET/SDH design problems
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
On the solution of a graph partitioning problem under capacity constraints
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hi-index | 0.00 |
We consider the problem of interconnecting a set of customer sites using bidirectional SONET rings of equal capacity. Each site is assigned to exactly one ring and a special ring, called the federal ring, interconnects the other rings together. The objective is to minimize the total cost of the network subject to a ring capacity limit where the capacity of a ring is determined by the total bandwidth required between sites assigned to the same ring plus the total bandwidth request between these sites and sites assigned to other rings.We present exact, integer-programming based solution techniques and fast heuristic algorithms for this problem. We compare the results from applying the heuristic algorithms with those produced by the exact methods for real-world as well as randomly generated problem instances. We show that two of the heuristics find solutions that cost at most twice that of an optimal solution. Empirical evidence indicates that in practice the algorithms perform much better than their theoretical bound and often find optimal solutions.