A MEMCIF-IN method for safety risk assessment in oil and gas industry based on interval numbers and risk attitudes. (October 2019)

• Record Type: Journal Article
• Authors: Tian, Donghong; Zhao, Chunlan; Wang, Bing; Zhou, Meng
• Is Part Of: Engineering applications of artificial intelligence. Volume 85(2019)
• Page Start: 269
• Page End: 283
• Publication Date: 2019-10
• Subjects: Risk assessment -- Risk matrix -- Interval numbers -- Risk attitude -- Multi-expert -- Multi-criterion.; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.06.015
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper mainly proposes a novel method to construct a risk matrix for assessing safety risks in oil and gas industry. There are often multiple experts and multiple criteria involved in safety risk assessment problems and the assessment data are often given in the form of interval numbers. In order to better assess risks, the definition of interval number with distribution function and utility function is proposed in this paper. The frequency and the consequence of risk are only two needed indicators in risk matrix and their values are needed in the form of crisp values. So a multi-expert and multi-criterion information fusion based on interval number(MEMCIF-IN) model is built in this paper. Firstly, a multi-expert and multi-criterion fusion model is constructed to combine individual interval numbers into a collective interval number and integrate multiple criteria into a comprehensive index. In the fusion model, the weights of assessment experts are calculated based on the objective weights and the subjective weights simultaneously and the information of individual interval numbers is preserved without information loss in the final result. Secondly, a Continuous Weighted Ordered Weighted Aggregation(C-WOWA) operator is proposed. In the C-WOWA operator, the position weights which are generated by utility function and the importance weights which are generated by probability density function are considered at the same time. The position weights in the C-WOWA operator can correct the impact of experts' risk attitudes and the importance weights can reflect the importance of the points themselves in an interval number. Finally, a risk matrix is constructed to show which risk is high and which is low . In addition, an application is implemented to show the practicality and rationality of the proposed method. Highlights: The assessment data are given in form of interval number with probability density function and utility function. The proposed MI-OWA operator can preserve all information from individual indicators. The proposed C-WOWA operator cannot only correct the impact of experts' risk attitudes but also incorporate the importance of the points themselves in an interval number. The proposed MEMCIF-IN method can deal with multi-expert and multi-criterion information fusion based on interval numbers effectively.