Resilience-based optimization of recovery strategies for network systems
01 Jul 2017-pp 1-6
TL;DR: In this paper, the authors describe two resilience models, quotient resilience and integral resilience, to measure the final recovered performance and the performance cumulative process during recovery respectively, and implement the optimization of the system recovery strategies after disruption, focusing on the repair sequence of the damaged components.
Abstract: Network systems, such as transportation systems and water supply systems, play important roles in our daily life and industrial production. However, a variety of disruptive events occur during their life time, causing a series of serious losses. Due to the inevitability of disruption, we should not only focus on improving the reliability or the resistance of the system, but also pay attention to the ability of the system to response timely and recover rapidly from disruptive events. That is to say we need to pay more attention to the resilience. In this paper, we describe two resilience models, quotient resilience and integral resilience, to measure the final recovered performance and the performance cumulative process during recovery respectively. Based on these two models, we implement the optimization of the system recovery strategies after disruption, focusing on the repair sequence of the damaged components and the allocation scheme of resource. The proposed research in this paper can serve as guidance to prioritize repair tasks and allocate resource reasonably.
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TL;DR: This paper proposes a unified method of calculating the reliability, availability and performability indices based on the definition of special forms of reward matrix and proves to be effective in calculating both cumulative and instantaneous measures in steady-state and transient cases.
Abstract: This paper discusses a unified approach to reliability, availability and performability analysis of complex engineering systems. Theoretical basis of this approach is continuous-time discrete state Markov processes with rewards. From reliability modeling point of view complex systems are the systems with static and dynamic redundancy, imperfect fault coverage, various recovery strategies, multilevel operation and varying severity of failure states. We propose a unified method of calculating the reliability, availability and performability indices based on the definition of special forms of reward matrix. This method proved to be effective in calculating both cumulative and instantaneous measures in steady-state and transient cases. We describe special analytical software which implements suggested method. We demonstrate the flexibility of the proposed method and software by analyzing multilevel process unit with protection and demand-based warm standby system.
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TL;DR: The traditional view of natural systems, therefore, might well be less a meaningful reality than a perceptual convenience.
Abstract: Individuals die, populations disappear, and species become extinct. That is one view of the world. But another view of the world concentrates not so much on presence or absence as upon the numbers of organisms and the degree of constancy of their numbers. These are two very different ways of viewing the behavior of systems and the usefulness of the view depends very much on the properties of the system concerned. If we are examining a particular device designed by the engineer to perform specific tasks under a rather narrow range of predictable external conditions, we are likely to be more concerned with consistent nonvariable performance in which slight departures from the performance goal are immediately counteracted. A quantitative view of the behavior of the system is, therefore, essential. With attention focused upon achieving constancy, the critical events seem to be the amplitude and frequency of oscillations. But if we are dealing with a system profoundly affected by changes external to it, and continually confronted by the unexpected, the constancy of its behavior becomes less important than the persistence of the relationships. Attention shifts, therefore, to the qualitative and to questions of existence or not. Our traditions of analysis in theoretical and empirical ecology have been largely inherited from developments in classical physics and its applied variants. Inevitably, there has been a tendency to emphasize the quantitative rather than the qualitative, for it is important in this tradition to know not just that a quantity is larger than another quantity, but precisely how much larger. It is similarly important, if a quantity fluctuates, to know its amplitude and period of fluctuation. But this orientation may simply reflect an analytic approach developed in one area because it was useful and then transferred to another where it may not be. Our traditional view of natural systems, therefore, might well be less a meaningful reality than a perceptual convenience. There can in some years be more owls and fewer mice and in others, the reverse. Fish populations wax and wane as a natural condition, and insect populations can range over extremes that only logarithmic
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