Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA.
U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA.
Risk Anal. 2019 Sep;39(9):1899-1912. doi: 10.1111/risa.13395.
Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other "ilities" directly in the value hierarchy, while the second method quantifies mission resilience and uses the "ilities" in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems-of-systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.
近年来，人们大力致力于对系统因环境和敌对威胁而受到破坏的恢复力进行建模和评估。研究人员在社会学、心理学、计算机网络和工程系统等多个学科领域对恢复力进行了研究。在评估工程系统的恢复力时，恢复力评估通常会考虑单个性能指标、一次破坏、性能损失、恢复所需的时间或这些元素的组合。我们定义并使用了一个弹性工程系统定义，将系统恢复力分为平台恢复力和任务恢复力。大多数复杂系统都有多个性能指标；本研究提出使用多目标决策分析，使用两种不同的方法来评估具有多个性能指标的系统的系统恢复力。第一种方法量化了平台恢复力，并直接将恢复力和其他“可用性”纳入价值层次结构中，而第二种方法量化了任务恢复力，并在价值层次结构中每个性能指标的预期任务性能的计算中使用“可用性”。我们使用一个具有不同连接性和自主性水平的交通系统网络的示例来说明任务恢复力方法，该网络具有不同水平的弹性，以及使用假设的军事示例来说明平台恢复力。我们的分析发现，有必要根据特定威胁下的特定任务和场景来量化性能，然后使用建模和模拟来帮助确定给定条件下系统的恢复力。该示例展示了如何将系统任务恢复力纳入其中，从而可以提高某些性能指标的性能，同时对其他性能指标产生负面影响。
