Abstract
The task to understand systemic functioning and predict the behavior of today’s sociotechnical systems is a major challenge facing researchers due to the nonlinearity, dynamicity, and uncertainty of such systems. Many variables can only be evaluated in terms of qualitative terms due to their vague nature and uncertainty. In the first stage of our project, we proposed the application of the Functional Resonance Analysis Method (FRAM), a recently emerging technique, to evaluate aircraft deicing operations from a systemic perspective. In the second stage, we proposed the integration of fuzzy logic into FRAM to construct a predictive assessment model capable of providing quantified outcomes to present more intersubjective and comprehensible results. The integration process of fuzzy logic was thorough and required significant effort due to the high number of input variables and the consequent large number of rules. In this paper, we aim to further improve the proposed prototype in the second stage by integrating rough sets as a data-mining tool to generate and reduce the size of the rule base and classify outcomes. Rough sets provide a mathematical framework suitable for deriving rules and decisions from uncertain and incomplete data. The mixed rough sets/fuzzy logic model was applied again here to the context of aircraft deicing operations, keeping the same settings as in the second stage to better compare both results. The obtained results were identical to the results of the second stage despite the significant reduction in size of the rule base. However, the presented model here is a simulated one constructed with ideal data sets accounting for all possible combinations of input variables, which resulted in maximum accuracy. The same should be further optimized and examined using real-world data to validate the results.
Highlights
Resilience Engineering is a discipline concerned with designing and constructing resilient systems, i.e., systems with the ability to cope with complexity and adapt with unforeseen changes and performance variability [1]
In the previous stage of our project [24], we addressed the lack of quantification and aimed at presenting a possible approach to overcome this limitation by integrating fuzzy logic into the framework of Functional Resonance Analysis Method (FRAM)
The flight was delayed as a result of high traffic volume and due to the weather conditions and the state of the runways, which affected the movement of the aircraft on the airport grounds
Summary
Resilience Engineering is a discipline concerned with designing and constructing resilient systems, i.e., systems with the ability to cope with complexity and adapt with unforeseen changes and performance variability [1]. Classical approaches are no longer sufficient in the age of complexity to provide a complete and comprehensive picture, and a trend shift occurred in recent years, leading to the introduction of innovative methods to present a systemic perspective in system’s analysis. One of the main methods in Resilience Engineering is the Functional Resonance Analysis Method (FRAM) [2,3]. The principles (Figure 1), on which FRAM rely, allow for understanding complex systemic behavior as a result of performance variability and its combinations. The idea in systemic approaches is that undesired outcomes are not and entirely explainable in terms of singular components’ failure, errors, or sequential events. The natural deviation in performance from prescribed procedures and Sustainability 2020, 12, 1918; doi:10.3390/su12051918 www.mdpi.com/journal/sustainability
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