- 2004.04493v1
Final published version, 1.49 MB, PDF document

Research output: Contribution to journal › Journal article

Published

**An Efficient Approach to Distributionally Robust Network Capacity Planning.** / Dokka Venkata Satyanaraya, Trivikram; Garuba, Francis; Goerigk, Marc; Jacko, Peter.

Research output: Contribution to journal › Journal article

Dokka Venkata Satyanaraya, T, Garuba, F, Goerigk, M & Jacko, P 2020, 'An Efficient Approach to Distributionally Robust Network Capacity Planning', *arXiv*.

Dokka Venkata Satyanaraya, T., Garuba, F., Goerigk, M., & Jacko, P. (2020). An Efficient Approach to Distributionally Robust Network Capacity Planning. *arXiv*.

Dokka Venkata Satyanaraya T, Garuba F, Goerigk M, Jacko P. An Efficient Approach to Distributionally Robust Network Capacity Planning. arXiv. 2020 Apr 9.

@article{24008333d6f644508e636c03a77ea1e4,

title = "An Efficient Approach to Distributionally Robust Network Capacity Planning",

abstract = " In this paper, we consider a network capacity expansion problem in the context of telecommunication networks, where there is uncertainty associated with the expected traffic demand. We employ a distributionally robust stochastic optimization (DRSO) framework where the ambiguity set of the uncertain demand distribution is constructed using the moments information, the mean and variance. The resulting DRSO problem is formulated as a bilevel optimization problem. We develop an efficient solution algorithm for this problem by characterizing the resulting worst-case two-point distribution, which allows us to reformulate the original problem as a convex optimization problem. In computational experiments the performance of this approach is compared to that of the robust optimization approach with a discrete uncertainty set. The results show that solutions from the DRSO model outperform the robust optimization approach on highly risk-averse performance metrics, whereas the robust solution is better on the less risk-averse metric. ",

keywords = "math.OC",

author = "{Dokka Venkata Satyanaraya}, Trivikram and Francis Garuba and Marc Goerigk and Peter Jacko",

year = "2020",

month = apr

day = "9",

language = "English",

journal = "arXiv",

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TY - JOUR

T1 - An Efficient Approach to Distributionally Robust Network Capacity Planning

AU - Dokka Venkata Satyanaraya, Trivikram

AU - Garuba, Francis

AU - Goerigk, Marc

AU - Jacko, Peter

PY - 2020/4/9

Y1 - 2020/4/9

N2 - In this paper, we consider a network capacity expansion problem in the context of telecommunication networks, where there is uncertainty associated with the expected traffic demand. We employ a distributionally robust stochastic optimization (DRSO) framework where the ambiguity set of the uncertain demand distribution is constructed using the moments information, the mean and variance. The resulting DRSO problem is formulated as a bilevel optimization problem. We develop an efficient solution algorithm for this problem by characterizing the resulting worst-case two-point distribution, which allows us to reformulate the original problem as a convex optimization problem. In computational experiments the performance of this approach is compared to that of the robust optimization approach with a discrete uncertainty set. The results show that solutions from the DRSO model outperform the robust optimization approach on highly risk-averse performance metrics, whereas the robust solution is better on the less risk-averse metric.

AB - In this paper, we consider a network capacity expansion problem in the context of telecommunication networks, where there is uncertainty associated with the expected traffic demand. We employ a distributionally robust stochastic optimization (DRSO) framework where the ambiguity set of the uncertain demand distribution is constructed using the moments information, the mean and variance. The resulting DRSO problem is formulated as a bilevel optimization problem. We develop an efficient solution algorithm for this problem by characterizing the resulting worst-case two-point distribution, which allows us to reformulate the original problem as a convex optimization problem. In computational experiments the performance of this approach is compared to that of the robust optimization approach with a discrete uncertainty set. The results show that solutions from the DRSO model outperform the robust optimization approach on highly risk-averse performance metrics, whereas the robust solution is better on the less risk-averse metric.

KW - math.OC

M3 - Journal article

JO - arXiv

JF - arXiv

ER -