Reformulation and decomposition approaches for traffic routing in optical networks

Reformulation and decomposition approaches for traffic routing in optical networks

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Article ID: iaor20162573
Volume: 67
Issue: 4
Start Page Number: 277
End Page Number: 298
Publication Date: Jul 2016
Journal: Networks
Authors: , ,
Keywords: networks: flow, networks: scheduling, design, demand, vehicle routing & scheduling
Abstract:

We consider a multilayer network design model arising from a real‐life telecommunication application where traffic routing decisions imply the installation of expensive nodal equipment. Customer requests come in the form of bandwidth reservations for a given origin destination pair. Bandwidth demands are expressed as multiples of nominal granularities. Each request must be single‐path routed. Grooming several requests on the same wavelength and multiplexing wavelengths in the same optical stream allow a more efficient use of network capacity. However, each addition or withdrawal of a request from a wavelength requires optical to electrical conversion and the use of cross‐connect equipment with expensive ports of high densities. The objective is to minimize the number of required ports of the cross‐connect equipment. We deal with backbone optical networks, therefore with networks with a moderate number of nodes (14 to 20) but thousands of requests. Further difficulties arise from the symmetries in wavelength assignment and traffic loading. Traditional multicommodity network flow approaches are not suited for this problem. Instead, four alternative models relying on Dantzig–Wolfe and/or Benders' decomposition are introduced and compared. The formulations are strengthened using symmetry breaking restrictions, variable domain reduction, zero‐one discretization of integer variables, and cutting planes. The resulting dual bounds are compared to the values of primal solutions obtained through hierarchical optimization and rounding procedures. For realistic size instances, our best approaches provide solutions with optimality gap of approximately 5% on average in around 2 h of computing time.

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