Fuzzy stochastic risk-based decision analysis with the mobile offshore base as a case study

Blair, Andrew N.; Ayyub, Bilal M.; Bender, William J.

doi:10.1016/s0951-8339(00)00027-7

Cited by 25 publications

(26 citation statements)

References 8 publications

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“…In addition, some other characteristics make risk assessment by fuzzy logic more applicable than classical methods. The main advantages of fuzzy system usage in risk assessment are considered as follows: -fuzzy logic risk assessment expresses the possibility of an outcome, but classical risk assessment methods estimate the likelihood of an outcome (Darbra et al, 2008a); -input and output relationships in fuzzy logic risk assessment are determined not by complicated equations, but by sets of logical rules (Blair et al, 2001); -fuzzy logic risk assessment can be used for cases with input data that is vague, imprecise and insufficient (Darbra and Casal, 2009); -results of risk assessment by fuzzy logic are easy for decision-making. In fact, managers can understand results and outgoings better and more precisely; -partial failures can be studied by fuzzy sets and make it possible to conduct study of risk in more detail and more real conditions.…”

Section: Fuzzy Logicmentioning

confidence: 99%

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Karimpour

Zarghami

Moosavian

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-A new risk assessment methodology by using fuzzy logic is proposed in this paper. The new Fuzzy Inference System (FIS) was established by the Mamdani algorithm based on different consequences of an incident. A combination of two FIS formed the proposed fuzzy method. Human knowledge and brainstorming were the devices for making the rules and interdependencies between variables in the new model. Different types of consequences and effective parameters were considered as inputs for the first fuzzy inference system. The final consequence was the preliminary result of the first inference model. It added to the probability of failures, as inputs of the second inference model. The result of the second inference model was the risk factor, which was considered as the final output of the proposed new fuzzy model. This model makes risk assessment more convenient in the absence of suitable data. In addition, decision-making will be easier, since its results are more understandable than the results of classical methods. A case study and a comparison between the classic method and the new fuzzy model illustrated that the results of the proposed model are more accurate, reliable and convenient for use in decision-making.Résumé -Nouveau modèle à logique floue pour une évaluation de risque basée sur différents types de conséquences -Une nouvelle méthode d'évaluation des risques en utilisant une logique floue est proposée dans le présent article. Le nouveau système d'inférence flou a été établi en utilisant l'algorithme de Mamdani basé sur les différentes conséquences d'un incident. Une combinaison de deux systèmes d'inférence flous a donné naissance à la méthode floue proposée. Les connaissances humaines et un brainstorming ont été les outils qui ont permis d'établir les règles et interdépendances entre des variables du nouveau modèle. Différents types de conséquences et de paramètres effectifs ont été pris en considération comme entrées pour le premier système d'inférence flou. La conséquence finale qui est le résultat préliminaire du premier modèle d'inférence, est combinée avec la probabilité de risques, en tant qu'entrées du second modèle d'inférence. Cette combinaison conduit à considérer le facteur de risque comme le résultat final du nouveau modèle flou proposé. Ce modèle rend l'évaluation des risques plus facile, en l'absence de données adaptées. En outre, la prise de décision sera plus simple, puisque ses résultats sont plus compréhensibles que les résultats des méthodes classiques. Une étude de cas et une comparaison entre la méthode classique et le nouveau modèle flou ont démontré que les résultats du modèle proposé étaient plus précis, plus fiables et plus faciles à utiliser dans la prise de décision.

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Section: Fuzzy Logicmentioning

confidence: 99%

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Karimpour

Zarghami

Moosavian

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…However, in realistic energy management systems, many parameters/coefficients as well as the irrelations may have uncertain natures with multiple dimensions and layers [9,10,[179][180][181][182][183]. Over the past decades, the most common approaches for dealing with uncertainties in optimization modeling included interval, stochastic, and fuzzy-set-based methods as well as their hybrids [9][10][11][184][185][186][187][188].…”

Section: Inexact Optimization Modelingmentioning

confidence: 99%

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

et al. 2011

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Abstract:Energy is crucial in supporting people's daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in OPEN ACCESSEnergies 2011, 4 1625 uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.

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“…Risk can be measured by pairing the probability of occurrence of an event, and the outcomes or consequences associated with that occurrence. This pairing is not a mathematical operation, a scalar or vector quantity, but a matching of the probability of the event occurring with the expected consequence [7].…”

Section: Risk Assessment and Risk Managementmentioning

confidence: 99%

“…It is therefore possible to classify the uncertainties associated with risk in two broad categories [17,7]: (1) stochastic (due to the randomness); and, (2) cognitive (due to the vagueness of expertÕs judgments). To accommodate these kinds of uncertainty, there are two main techniques: (1) probability theory (for stochastic uncertainties); and, (2) possibilistic theory (i.e.…”

Section: Dealing With the Uncertaintymentioning

confidence: 99%