Industrial optimization applications must be "robust," i.e., they

must provide good solutions to problem instances of different size and

numerical characteristics, and continue to work well when side constraints

are added. We present a case study that addresses this requirement

and its consequences on the applicability of different optimization

techniques to a network design problem. 

An extensive benchmark suite, built on real network design data,

is used to test multiple algorithms for robustness against variations in

problem size, numerical characteristics, and side constraints. The

experimental results illustrate the performance discrepancies that have

occurred and how some have been corrected.