A Comparison of Models for Uncertain Network Design

Garuba, Francis and Goerigk, Marc and Jacko, Peter (2019) A Comparison of Models for Uncertain Network Design. In: 30th European Conference on Operational Research, 2019-06-232019-06-26, University College Dublin.

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Abstract

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.

Item Type:
Contribution to Conference (Paper)
Journal or Publication Title:
30th European Conference on Operational Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/aacsb/disciplinebasedresearch
Subjects:
ID Code:
138962
Deposited By:
Deposited On:
06 Dec 2019 16:25
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Jul 2020 00:53