Publication Date:
2020-08-05
Description:
Traffic in communication networks fluctuates heavily over time.
Thus, to avoid capacity bottlenecks, operators highly overestimate
the traffic volume during network planning. In this paper we
consider telecommunication network design under traffic uncertainty,
adapting the robust optimization approach of Bertsimas and Sim [2004]. We
present three different mathematical formulations for this problem,
provide valid inequalities, study the computational implications,
and evaluate the realized robustness.
To enhance the performance of the mixed-integer programming solver
we derive robust cutset inequalities generalizing their
deterministic counterparts. Instead of a single cutset inequality
for every network cut, we derive multiple valid
inequalities by exploiting the extra variables available in the
robust formulations. We show that these inequalities define facets
under certain conditions and that they completely describe a projection
of the robust cutset polyhedron if the cutset consists of a single edge.
For realistic networks and live traffic measurements we compare the
formulations and report on the speed up by the valid inequalities.
We study the "price of robustness" and evaluate the
approach by analyzing the real network load. The results show that
the robust optimization approach has the potential to support
network planners better than present methods.
Language:
English
Type:
reportzib
,
doc-type:preprint
Format:
application/pdf
