The risk assessment is one of the most significant procedures for identifying, preventing, and controlling Occupational Health and Safety (OHS) risks. One of many kinds of techniques for OHS risk assessment is based on the Fine-Kinney model. Most of the Fine-Kinney-based risk assessment approaches can consider the relative importance degree of risk parameters. Nevertheless, the current Fine-Kinney-based risk assessment approaches do not have abilities to capture the reference dependence effects and detailed relationships among hazards. In addition, these approaches overlook the influence of the deviation of risk evaluation information. To overcome these limitations, in this paper, an improved Fine-Kinney model is proposed for OHS risk assessment by integrating the weighted power average (WPA) operator, ORESTE (Organísation, rangement et Synthèse de données relarionnelles (in French)) method, and cumulative prospect theory. First, the interval 2-Tuple linguistic variables are adopted to transform linguistic risk information into quantitative risk rating information. Then, an extended WPA operator is proposed to fuse the risk evaluation information from decision-makers, in which an optimization model is constructed to determine the weights of decision-makers. Next, an extended ORESTE method based on cumulative prospect theory and interval 2-Tuple linguistic variables is incorporated into the Fine-Kinney model to prioritize OHS risk. After that, the OHS risk assessment of the automobile components manufacturing process is presented to test the applicability and rationality of the improved Fine-Kinney model. After that, a sensitivity analysis is conducted to further illustrate the proposed model. Finally, the comparative analyses between the proposed risk assessment approach and other Fine-Kinney models are led to illustrating its effectiveness and advantages.

风险评估是识别、预防和控制职业健康与安全 (OHS) 风险的最重要程序之一。OHS 风险评估的多种技术之一是基于 Fine-Kinney 模型。大多数基于 Fine-Kinney 的风险评估方法都可以考虑风险参数的相对重要性。然而，当前基于 Fine-Kinney 的风险评估方法无法捕捉参考依赖效应和危害之间的详细关系。此外，这些方法忽略了风险评估信息偏差的影响。为了克服这些限制，在本文中，通过整合加权平均功率 (WPA) 算子 ORESTE (Organísation, rangement et Synthèse de données relarionnelles（法语））方法和累积前景理论。首先，采用区间二元组语言变量将语言风险信息转化为定量风险评级信息。然后，提出了一种扩展的WPA算子来融合决策者的风险评估信息，构建优化模型来确定决策者的权重。接下来，将基于累积前景理论和区间 2 元组语言变量的扩展 ORESTE 方法纳入 Fine-Kinney 模型以优先考虑 OHS 风险。之后，提出了汽车零部件制造过程的OHS风险评估，以检验改进Fine-Kinney模型的适用性和合理性。在那之后，进行了敏感性分析以进一步说明提议的模型。最后，所提出的风险评估方法与其他 Fine-Kinney 模型之间的比较分析表明其有效性和优势。