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A Comparison of Models for Uncertain Network Design

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Publication date23/06/2019
Number of pages20
<mark>Original language</mark>English
Event30th European Conference on Operational Research - University College Dublin, Dublin, Ireland
Duration: 23/06/201926/06/2019


Conference30th European Conference on Operational Research
Abbreviated titleEURO XXX
Internet address


To solve a real-world problem, the modeler usually needs to make a trade-off between model complexity and usefulness. This is also true for robust optimization, where a wide range of models for uncertainty, so-called uncertainty sets, have been proposed. However, while these sets have been mainly studied from a theoretical perspective, there is little research comparing different sets regarding their usefulness for a real-world problem.
In this paper we consider a network design problem in a telecommunications context. We need to invest into the infrastructure, such that there is sufficient capacity for future demand which is not known with certainty. There is a penalty for an unsatisfied realized demand, which needs to be outsourced. We consider three approaches to model demand: using a discrete uncertainty set, using a polyhedral uncertainty set, and using the mean of a per-commodity fitted zero-inflated uniform distribution. While the first two models are used as part of a robust optimization setting, the last model represents a simple stochastic optimization setting. We compare these approaches on an efficiency frontier real-world data taken from the online library SNDlib and observe that, contrary to current research trends, robust optimization using the polyhedral uncertainty set may result in less efficient solutions.