Abstract

Filterless optical network has been widely used in recent years. The incentive of this technology is only the passive equipment will be used, which requires no electricity. By using this technology, not only the cost reductions, but also the environment preservation will be achieved.

In literature, a lot of researchers studied the design of filterless optical network. But due to the complexity and scalability limits of this problem, most of the works are based on heuristic or meta-heuristic methods. We were seeking exact solutions to achieve the design of filterless optical networks.

First we proposed a one step solution scheme, which combines tree decomposition and network provisioning, i.e.,routing and wavelength assignment within a single mathematical model, called CG_FOP. We propose a decomposition with two different sub-problems, which are solved alternately, in order to design an exact solution scheme. The first sub-problem generates filterless sub-nets while the second deals with their wavelength allocation.

Due to the complexity of the problem, significant time will be consumed if applied our model on a large and more connected network. In order to improve the performance, we proposed Dantzig-Wolfe decomposition model, called DW_FOP in which the sub-problem consists in generating a potential filterless optical sub-network, with a directed tree topology. In this new model, single pricing problem was formed which compute the network provisioning along with wavelength assignment together. In this way, master problem would be simplified, no longer contains complicated logic to build conflicts among requests. With this approach, computation time significantly reduced.

To further improve the design, we proposed a nested column generation model, called NCG_FOP, in order to speed up the solution process. We break down the solution into two level of pricing, the upper level pricing computes selected paths which assigned to granted requests, network provisioning and wavelength assignment for granted requests. The upper level pricing itself is a column generation process, which includes a lower level pricing generated improved path for each granted requests.