Fuzzy inference system for the risk assessment of liquefied natural gas carriers during loading/offloading at terminals

Abstract

A multiple attribute risk assessment approach using a fuzzy inference system is developed in this work. The approach is based on the use of fuzzy sets, a rule base and a fuzzy inference engine. Traditional input probabilities and consequences used in risk assessment are represented by fuzzy sets to take into account uncertainties associated with the assignment of their values. The output risk values can be presented as crisp values or fuzzy sets with associated degree of membership. The fuzzy inference system FIS is used as an alternative approach to qualitative risk matrix techniques currently used in many industries and by ship classification societies. Two approaches for fuzzy inference are adopted. These include the Mamdani approach in which output risk values are fuzzy sets and the Sugeno method of fuzzy inference, in which output risk values are constant or linear. The use of a fuzzy set approach is particularly suited for handling multiple attribute risk problems with imprecise data. It improves upon existing qualitative methods and allows the ranking of risk alternatives based on a unified fuzzy risk index measure. Results show that while the Mamdani method is intuitive and well suited to human input, the Sugeno method is computationally more efficient and guarantees continuity of the final risk output surface. Results also show that computed risk values using a fuzzy risk index measure are consistent with those obtained using a qualitative risk matrix approach. The proposed methodology is also applicable to other ship operating modes such as transit in open sea and/or entering/leaving port. A case study for a liquefied natural gas LNG ship loading/offloading at the terminal is presented to demonstrate the developed approach capability.