Optimal design of reliable network systems in presence of uncertainty
Optimal design of reliable network systems in presence of uncertainty
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DOI：
10.1109/tr.2005.847279
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发表时间：
2005-05
影响因子：
5.9
通讯作者：
M. Marseguerra;E. Zio;L. Podofillini;D. Coit
中科院分区：
文献类型：
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作者：
M. Marseguerra;E. Zio;L. Podofillini;D. Coit
在实践中，网络设计可以基于冗余配置的多种选择，以及可用于形成链路的不同可用组件。更具体地说，可以通过冗余分配来提高网络系统的可靠性，或者对于固定的网络拓扑，可以在有限的总体预算和其他约束条件下，通过在节点对之间选择高度可靠的链路来提高网络系统的可靠性。选择一种优选的网络系统设计需要对其可靠性进行估计。然而，在决策过程中也必须考虑与这种估计有关的不确定性。事实上，网络系统的可靠性通常是通过对受不确定性影响的下层组件（节点和链路）的可靠性的估计来估计的。不确定性的传播降低了系统可靠性估计的准确性。本文提出了一种多目标优化方法，以组件类型、不确定可靠性和冗余水平为决策变量时，以最大化网络可靠性估计，最小化其相关方差为目标。在提出的方法中，遗传算法（GA）和蒙特卡罗（MC）模拟有效地结合起来，以确定相对于既定目标的最优网络设计。得到了一组Pareto最优解，使决策者能够灵活地选择最能满足其风险分布的折衷解。本文采用该方法对样本网络进行了求解。结果表明，当该公式将估计不确定性纳入优化设计问题目标时，得到了显著不同的设计结果。
In practice, network designs can be based on multiple choices of redundant configurations, and different available components which can be used to form links. More specifically, the reliability of a network system can be improved through redundancy allocation, or for a fixed network topology, by selection of highly reliable links between node pairs, yet with limited overall budgets, and other constraints as well. The choice of a preferred network system design requires the estimation of its reliability. However, the uncertainty associated with such estimates must also be considered in the decision process. Indeed, network system reliability is generally estimated from estimates of the reliability of lower-level components (nodes & links) affected by uncertainties. The propagation of the estimation uncertainty from the components degrades the accuracy of the system reliability estimation. This paper formulates a multiple-objective optimization approach aimed at maximizing the network reliability estimate, and minimizing its associated variance when component types, with uncertain reliability, and redundancy levels are the decision variables. In the proposed approach, Genetic Algorithms (GA) and Monte Carlo (MC) simulation are effectively combined to identify optimal network designs with respect to the stated objectives. A set of Pareto optimal solutions are obtained so that the decision-makers have the flexibility to choose the compromised solution which best satisfies their risk profiles. Sample networks are solved in the paper using the proposed approach. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty into the optimal design problem objectives.