This paper presents a new method and a constraint-based objective function to solve two problemsrelated to the design of optical telecommunication networks, namely the Synchronous Optical Net-work Ring Assignment Problem (SRAP) and the Intra-ring Synchronous Optical Network DesignProblem (IDP). These network topology problems can be represented as a graph partitioning withcapacity constraints as shown in previous works. We present here a new objective function and anew local search algorithm to solve these problems. Experiments conducted in C
In order to compute a starting solution for the IDP problem, the authors describe four different heuristics. The first heuristic introduced in @cite_5 ordered the edges by decreasing weight, at each iteration it tries to assign the edge with the biggest weight which is not already assigned, to the ring with the smallest residual capacity regarding to capacity constraint. If no assignment is possible, the current edge is assigned to a new ring. The second one, sorts the edges by increasing weight, and tries to assign the current edge to the current ring if the capacity constraint is respected, otherwise the ring is no longer considered and a new ring is initialized with the current edge.
@cite_5
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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.