Ranking Alternatives on the Basis of the Intensity of Dominance and Fuzzy Logic within MAUT

We introduce dominance measuring methods to derive a ranking of alternatives to deal with incomplete information in multi-criteria decision making problems on the basis of Multi-Attribute Utility Theory (MAUT). We consider the situation where the alternative performances are represented by uniformly distributed intervals, and there exists imprecision concerning the decision-makers¿ preferences, by means of classes of individual utility functions and imprecise weights represented by weight intervals or fuzzy weights, respectively. An additive multi-attribute utility model is used to evaluate the alternatives under consideration, which is considered a valid approach in most practical cases. The approaches we propose are based on the dominance values between pairs of alternatives that can be computed by linear programming, which are then transformed into dominance intensities from which a dominance intensity measure is derived. The methods proposed are compared with other existing dominance measuring methods and other methodologies by Monte Carlo simulation techniques. The performance is analyzed in terms of two measures of efficacy: hit ratio, the proportion of all cases in which the method selects the same best alternative as in the TRUE ranking, and the Rank-order correlation, which represents how similar the overall rank structures of alternatives are in the TRUE ranking and in the ranking derived from the method. The approaches are illustrated with an example consisting on the selection of intervention strategies to restore an aquatic ecosystem contaminated by radionuclides

Ordenación de las alternativas basándose en la intesidad de dominancia y la lógica difusa

Se introduce un nuevo método de ordenación de las alternativas en un problema de decisión multicriterio con imprecisión en la información proporcionada por el decisor, representada por una función de utilidad multiatributo aditiva. Donde las consecuencias de las alternativas se representan mediante distribuciones uniformes, las funciones de utilidad de cada atributo son clases de funciones de utilidad y los pesos asociados a los atributos son números difusos triangulares (trapezoidales)

Fuzzy evidence theory and Bayesian networks for process systems risk analysis

YesQuantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.The research of Sohag Kabir was partly funded by the DEIS project (Grant Agreement 732242)

Entwicklung einer indikatorenbasierten Methodik zur Vulnerabilitätsanalyse für die Bewertung von Risiken in der industriellen Produktion

Die Arbeit stellt die Entwicklung einer Methode zur Bewertung externer industrieller Risiken vor, die der Analyse indirekter Risikoeffekte auf räumlicher Ebene dient und verschiedene Risikoarten berücksichtigt. Zur Bewertung des industriellen Risikos wird die Analyse der Vulnerabilität herangezogen und über ein hierarchisches Indikatorenmodell abgebildet. Um Auswirkungen von Abhängigkeiten und Unsicherheiten zu berücksichtigen, werden Methoden zur Abhängigkeits- und Sensitivitätsanalyse entwickelt