论文标题
有效的分配稳健网络能力计划的方法
An Efficient Approach to Distributionally Robust Network Capacity Planning
论文作者
论文摘要
在本文中，我们考虑了电信网络中的网络容量扩展问题，那里存在与预期的交通需求相关的不确定性。我们采用分布强大的随机优化（DRSO）框架，其中不确定需求分布的歧义集是使用矩信息，均值和方差构建的。由此产生的DRSO问题被提出为双层优化问题。我们通过表征由此产生的最坏情况的两点分布来为此问题开发有效的解决方案算法，这使我们能够将原始问题重新制定为凸优化问题。 在计算实验中，将此方法的性能与具有离散的不确定性集的强大优化方法进行了比较。结果表明，DRSO模型的解决方案优于高度避免风险的性能指标的强大优化方法，而在较小的规避风险的度量方面，强大的解决方案更好。
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.
