  In a telecommunication network, Routing and Wavelength Assignment (RWA) is
the problem of finding lightpaths for incoming connection requests. When facing
a dynamic traffic, greedy assignment of lightpaths to incoming requests based
on predefined deterministic policies leads to a fragmented network that cannot
make use of its full capacity due to stranded bandwidth. At this point service
providers try to recover the capacity via a defragmentation process. We study
this setting from two perspectives: (i) while granting the connection requests
via the RWA problem and (ii) during the defragmentation process by lightpath
rerouting. For both problems, we present the first two-stage stochastic integer
programming model incorporating incoming request uncertainty to maximize the
expected grade of service. We develop a decomposition-based solution approach,
which uses various relaxations of the problem and a newly developed
problem-specific cut family. Simulation of two-stage policies for a variety of
instances in a rolling-horizon framework of 52 stages shows that our stochastic
models provide high-quality solutions compared to traditionally used
deterministic ones. Specifically, the proposed provisioning policies yield
improvements of up to 19% in overall grade of service and 20% in spectrum
saving, while the stochastic lightpath rerouting policies grant up to 36% more
requests using up to just 4% more bandwidth spectrum.

Optimization and Control