## 1 Introduction  
There are many methods used to evaluate ergonomic reliability. The first-generation research methods that focused on human behavior and classification of errors have appeared and developed into the second-generation methods seen later, such as cognitive reliability and error analysis method (CREAM). Like many new approaches, CREAM incorporates cognitive models representing the more complex human reliability analysis (HRA) [1]. Nowadays, ergonomic reliability is commonly applied in the medical field, and a study by [PERSON] et al. indicated that HRA would play a central role in medical security [2]. [PERSON] et al. noted that human-machine assessment can play an effective role in risk analysis in the healthcare field; however, the technique needs to be adapted to take into account the context of the field [3]. [PERSON] et al. applied the structure function in reliability engineering to analyze and evaluate human factors in the use of medical devices [4]. [PERSON] et al. demonstrated the importance of human reliability assessment in laparoscopic ablation [5]. These studies indicate that reliability analysis is not limited to traditional mechanical field applications, such as the stress testing of mechanical structures etc. [6; 7; 8]; reliability analysis is also widely used in the medical field, nowadays, although some reliability analysis methods applied in this field are immature. In addition, CREAM is gradually being introduced into the medical field as a typical human reliability approach. [PERSON] et al. studied a derivative model based on CREAM to predict the dispensing error rate in pharmacies [9]; however, the model has limitations that do not take into account the source of system errors other than human factors. Modern engineering systems often operate under complex operating conditions [10]; thus, reliability analysis often involves multidisciplinary coupling and multi-level structure construction [11] with uncertain factors [12; 13]. CREAM, which has been utilized in many optimization schemes, is very suitable for ergonomic reliability analysis in the above situations. [PERSON] et al. used a fuzzy analytic hierarchy process (FAHP) to evaluate the weight of common performance conditions (CPCs) in the shipping industry, and proposed a method where CREAM could be quantified to make it more suitable for the actual situation of personnel in the shipping industry [14]. [PERSON] et al. studied the human error in diving operations by combining CREAM and Bayesian networks [15]. Based on the extended CREAM framework, [PERSON] et al. optimized the CPC assessment under the chemical enterprise scenario on the basis of summarizing the safety management assessment of hundreds of hazardous chemical enterprises [16]. [PERSON] proposed a new fuzzy method to improve the logical relationship between CPC and cognitive control mode (COCOM) by adjusting the related weights, and verified it by taking an oil tanker as an example [17]. [PERSON] et al. proposed a prospective quantitative process simplification of CREAM [18]. [PERSON] et al. mentioned that the decision of CPC level or score is disturbed by uncertainty and proposed a fuzzy set method to capture such uncertainty [19]. [PERSON] et al. established a CREAM framework table for quantification of coal mine operation scenarios to solve its shortcomings, such as not considering the applicability of general performance conditions and the subjectivity of evaluating the CPC table [20]. The objects of systematic design optimization often have multi-objective characteristics, among which the determination and allocation of weights play an important role in the design result [21]; however, the predictions of traditional CREAM do not take into account how the system will change when targeted to the different cases.