An extended gained and lost dominance score method based risk prioritization for Fine-Kinney model with interval type-2 fuzzy information

Abstract

The Fine-Kinney model is one of the most extensively adopted classical occupational risk analysis techniques which has been regarded as a helpful means for addressing the risk identification and evaluation problems in various fields. However, current extended Fine-Kinney models have limited capacity to cope with occupational risk evaluation and prioritization problems considering interactive relationships among multi-risk parameters under inter-uncertain and intra-uncertain environments. To overcome the limitations, this paper proposes a hybrid risk prioritization approach for the Fine-Kinney model by integrating GLDS (gained and lost dominance score) method, interval type-2 fuzzy sets (IT2FSs), and Maclaurin symmetric mean (MSM) operator. First, the trapezoidal interval type-2 fuzzy numbers (TrIT2FNs) are introduced to depict the inter-uncertain and intra-uncertain risk evaluation information. Then, an ordered weighted averaging (OWA) operator is constructed to aggregate the complex and uncertain risk evaluation information from heterogeneous decision-makers in which the similarity-measure-based weighting method is incorporated. Next, the GLDS method combined with the weighted MSM operator is developed to address the risk prioritization problem in the Fine-Kinney model by considering the inter-dependencies among multi-risk parameters. Finally, a numerical example of occupational risk evaluation for potential hazards in construction excavation is presented within IT2FSs to demonstrate the application and availability of the extended Fine-Kinney model. After that, the comparison and sensitivity analyses are conducted to further illustrate the superiority and rationality of the proposed approach.

Keywords:

• Fine-Kinney
• risk prioritization
• gained and lost dominance score method
• interval type-2 fuzzy set

Disclosure statement

There are no relevant financial or non-financial competing interests among these authors.

Additional information

Funding

This work was supported by the National Science Foundation of China (72101004), the MOE Ministry of Education in China (MOE), Project of Humanities and Social Sciences (19YJC630160).