Anticipating the Chaotic Behaviour of Industrial Systems Based on Stochastic, Event-Driven Simulations

Bruzzone, Agostino G.; Revetria, Roberto; Simeoni, Simone; Viazzo, Simone; Orsoni, Alessandra

doi:10.1063/1.1787359

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Sistemdeki stokastik olayların etkisinin incelenmesi ve kaotik davranışlara ilişkin risk değerlendirmesi üzerine yapılan çalışmalar, acil durum planı üzerine tahmin yapmaya yardımcı olacaktır [36]. Bu yönüyle kaos teorisi bir yönetim metodolojisi olarak da kullanılmaktadır.…”

Section: Bir Tedarik Zinciri Yönetim Metodolojisi Olarak Kaos Teorisi (Chaos Theory As a Supply Chain Management Methodology)unclassified

Tedarik Zincirinde Kaos: Bir Literatür Taraması

Açıkgöz¹,

Çağıl²

2019

Academic Platform Journal of Engineering and Science

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The supply chain is a dynamic system of interactions between many assets and factors in the process from the supplier to the end customer until the product or service reaches the end customer. The increasing number of customers and their changing relationships with time make this dynamic system more complex. The complexity of this growing system leads to unpredictable problems. Many factors that can be observed or not observed as the cause of this complexity can be shown. In the global world where the competitive environment is increasing, companies see this as the best way to solve this complexity as the perfect functioning of every link in the supply chain. The effects of a number of factors that are ignored and thought to be small enough to have no effect in the supply chain have been demonstrated by analysis. A small hitch in any part of the chain can lead to an inevitable chaos in the supply chain. A slight change or an error in initial conditions due to unpredictability will cause unexpected large fluctuations and difficulties in the results of supply chain management. According to some studies in the literature, the N AÇIKGÖZ

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Section: Bir Tedarik Zinciri Yönetim Metodolojisi Olarak Kaos Teorisi (Chaos Theory As a Supply Chain Management Methodology)unclassified

Tedarik Zincirinde Kaos: Bir Literatür Taraması

Açıkgöz¹,

Çağıl²

2019

Academic Platform Journal of Engineering and Science

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“…For this reason, application of risk analsys is limited to simplified case studies. Examples of suitable case studies include the analysis of the chaotic behavior of a complex industrial system in terms of stochastic variations in its technical parameters (Bruzzone, 2004) and the analysis of existing multidisciplinary relations in the context of project management environment (Biffl, Moser, & Winkler, 2010).…”

Section: Risk Analysismentioning

confidence: 99%

“…It is therefore expected that for Smartgrids, the agent-based approach can be used for studying only specific geographically limited areas of vulnerability in the system (Kroger & Zio, 2011). (Grimm & Railsback, 2006;Wolf, Holvoet, & Leuven, 2003) Emergence No No Yes (Kroger & Zio, 2011;Schlapfer, Kessler, & Kroger, 2008) Chaos Only for engineering analysis (Bruzzone, 2004) No Yes (Wolf, Holvoet, & Leuven, 2003) Multidisciplinary relations…”

Section: Agent-based Modeling and Simulationmentioning

confidence: 99%

Complexity and vulnerability of Smartgrid systems

Kuznetsova¹,

Culver²,

Zio³

2011

Advances in Safety, Reliability and Risk Management

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for the Proceedings of the European Safety and Reliability Conference the author version, submitted here, was reducedInternational audienceIn this paper, we look at the complexity and related vulnerability characteristics of Smartgrids. Typical characteristics of complex systems, such as self-organization, emergence, chaotic behavior and evolution, are considered with respect to Smartgrids as future energy infrastructures. These characteristics are categorized as inherent, challenge-response, or acquired. This guides the identification of major sources of uncertainty in the infrastructure. Topological and behavioral characteristics of Smartgrids are also explored with the aim of identifying potential vulnerabilities. We additionally discuss the assumptions, limitations and degree of precision of Smartgrids modeling

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