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A resource allocation approach for managing critical network-based infrastructure systems

机译:一种用于管理关键的基于网络的基础架构系统的资源分配方法

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In recent years, many resource allocation models have been developed to protect critical infrastructure by maximizing system resiliency or minimizing its vulnerability to disasters or disruptions. However, these are often computationally intensive and require simplifying assumptions and approximations. In this study, we develop a robust and representative, yet tractable, model for optimizing maintenance planning of generic network-structured systems (transportation, water, power, communication). The proposed modeling framework examines models that consider both linear and nonlinear objective functions and enhances their structure through suitable manipulations. Moreover, the designed models inherently capture the network topography and the stochastic nature of disruptions and can be applied to network-structured systems where performance is assessed based on network flow efficiency and mobility. The developed models are applied to the Istanbul highway system in order to assess their relative computational effectiveness and robustness using several test cases that consider single- and multiple-treatment types, and the problems are solved on the NEOS server using different available software. The results demonstrate that our models are capable of obtaining optimal solutions within a very short time. Furthermore, the linear model is shown to yield a good approximation to the nonlinear model (it determined solutions within 0.3% of optimality, on average). Managerial insights are provided in regard to the optimal policies obtained, which generally appear to favor selecting fewer links and applying a higher quality treatment to them.
机译:近年来,已经开发出许多资源分配模型来通过最大化系统弹性或最小化其对灾难或破坏的脆弱性来保护关键基础架构。但是,这些通常需要大量计算,并且需要简化假设和近似值。在这项研究中,我们开发了一个健壮且有代表性但易于处理的模型,用于优化通用网络结构系统(运输,水,电,通讯)的维护计划。提出的建模框架检查了同时考虑线性和非线性目标函数的模型,并通过适当的操作来增强其结构。此外,设计的模型可以固有地捕获网络拓扑和中断的随机性,并且可以应用于基于网络流效率和移动性评估性能的网络结构系统。将开发的模型应用于伊斯坦布尔高速公路系统,以便使用考虑了单处理和多处理类型的几个测试用例来评估它们的相对计算效率和鲁棒性,并且使用不同的可用软件在NEOS服务器上解决了这些问题。结果表明,我们的模型能够在很短的时间内获得最佳解决方案。此外,线性模型显示出可以很好地近似非线性模型(它确定的解决方案平均在优化的0.3%之内)。提供了有关获得的最佳策略的管理见识,这些策略通常似乎倾向于选择较少的链接并对其进行更高质量的处理。

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