A flexible risk assessment approach integrating subjective and objective weights under uncertainty. (August 2021)

• Record Type: Journal Article
• Authors: Wen, Ta-Chun; Chung, Hsiang-Yu; Chang, Kuei-Hu; Li, Zong-Sian
• Is Part Of: Engineering applications of artificial intelligence. Volume 103(2021)
• Publication Date: 2021-08
• Subjects: Risk management -- Subjective and objective weights -- Hesitant information -- Missing information -- Failure mode and effects analysis -- Hesitant fuzzy linguistic term set; 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.2021.104310
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Risk assessment plays a critical role in the product design, manufacture and production process for different engineering fields. Traditionally, risk assessment-related issues are addressed using the failure mode and effects analysis approach to perform reliability analysis. However, information provided by experts in the risk assessment process often includes complete information, incomplete information and hesitant information at the same time. The traditional failure mode and effects analysis approach cannot handle incomplete information and hesitant information in the risk assessment process. Moreover, the traditional failure mode and effects analysis approach does not take into account the subjective and objective weights of the risk factors. In order to effectively overcome the limitations of the traditional failure mode and effects analysis method, this paper proposes a novel flexible risk assessment approach integrating subjective and objective weights under uncertainty. For handling the incomplete information, the proposed method uses currently known information to fill in the missing information. On the other hand, the proposed method uses hesitant fuzzy linguistic term set instead of single linguistic term set to handle hesitant information in the risk assessment process. Moreover, the proposed uses the concept of statistical variance to calculate the objective weights of the risk factors. An illustrative example of the screen printing stage of photovoltaic cell manufacturing is adopted to demonstrate the rationality and practicability of the proposed method. This paper also compares the risk priority results of the proposed method with those of the traditional FMEA method, the hesitant fuzzy linguistic term set method, and the Rao and Patel (2010) method. The simulation results indicate that the proposed can provide more correctly rank the identified causes of failure. The proposed not only simultaneously considers the subjective and objective weights for the risk factors, but also can handle hesitant information and missing information of the assessment attribute values.