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A Comparison of Models for Uncertain Network Design

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

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A Comparison of Models for Uncertain Network Design. / Garuba, Francis; Goerigk, Marc; Jacko, Peter.

2019. Paper presented at 30th European Conference on Operational Research, Dublin, Ireland.

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

Harvard

Garuba, F, Goerigk, M & Jacko, P 2019, 'A Comparison of Models for Uncertain Network Design', Paper presented at 30th European Conference on Operational Research, Dublin, Ireland, 23/06/19 - 26/06/19.

APA

Garuba, F., Goerigk, M., & Jacko, P. (2019). A Comparison of Models for Uncertain Network Design. Paper presented at 30th European Conference on Operational Research, Dublin, Ireland.

Vancouver

Garuba F, Goerigk M, Jacko P. A Comparison of Models for Uncertain Network Design. 2019. Paper presented at 30th European Conference on Operational Research, Dublin, Ireland.

Author

Garuba, Francis ; Goerigk, Marc ; Jacko, Peter. / A Comparison of Models for Uncertain Network Design. Paper presented at 30th European Conference on Operational Research, Dublin, Ireland.20 p.

Bibtex

@conference{34e6d9883a1b4346ad42205a444370e7,
title = "A Comparison of Models for Uncertain Network Design",
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. ",
keywords = "network design, robust optimization, optimization in telecommunications",
author = "Francis Garuba and Marc Goerigk and Peter Jacko",
year = "2019",
month = jun
day = "23",
language = "English",
note = "30th European Conference on Operational Research, EURO XXX ; Conference date: 23-06-2019 Through 26-06-2019",
url = "https://www.euro2019dublin.com/",

}

RIS

TY - CONF

T1 - A Comparison of Models for Uncertain Network Design

AU - Garuba, Francis

AU - Goerigk, Marc

AU - Jacko, Peter

PY - 2019/6/23

Y1 - 2019/6/23

N2 - 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.

AB - 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.

KW - network design

KW - robust optimization

KW - optimization in telecommunications

M3 - Conference paper

T2 - 30th European Conference on Operational Research

Y2 - 23 June 2019 through 26 June 2019

ER -