Utilizing a maximin optimization approach to maximize system resiliency

Abstract

Complex systems typically have a large parameter space and by definition have the potential for strong emergent behavior, especially when operating in a complex environment. A method is needed to design a system that will be resilient in a complex target operating environment. Here, we propose a maximin optimization approach to support the exploration of system design, based on co-evolutionary system/environment models, which emphasizes the importance of the operating environment in the system evolution, resulting in increased system resilience. In this paper, this method is demonstrated on an air defense system and shows an increase in resilience when compared to the system resulting from the application of a genetic algorithm such as has been previously demonstrated for other systems. For the air defense system used here, the proposed maximin optimization process significantly (p < .001, via a one-sided paired t-test) increased the resulting system resilience from 0.64 (the resilience of the system designed by a genetic algorithm) to 0.92 (the resilience of the system designed by maximin optimization).

An Improved Intelligent Auction Mechanism for Emergency Material Delivery

“…The core of time-dependent models is that the performance of systems (e.g., power transmission systems [33] and communication systems [34]) is time-dependent and can be expressed quantitatively, and such models usually describe the resilience of the system in terms of performance change curves. In contrast, time-independent models are mostly used to describe the system capability performance only calculated after the completion of the task, such as air defense systems [35]. The integral resilience model is commonly used for quantitative description of resilience, which describes the system resilience by comparing the cumulative distributional impact of system performance over time with and without interference, and can be found in the literature [36].…”