# An Integrated CREAM for Human Reliability Analysis Based on Consensus Reaching Process under Probabilistic Linguistic Environment

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Preliminaries

**Definition**

**1**

**[41].**Let $S=\left\{{s}_{i}|i=0,1,\dots ,2t\right\}$ be a totally ordered and finite discrete term set, ${s}_{i}$ be a possible value of a linguistic variable, then the linguistic term set S satisfies the following properties:

- (1)
- The set is ordered as: ${s}_{i}\ge {s}_{j}$ if $i\ge j$;
- (2)
- The negation operator is defined as: $neg\left({s}_{i}\right)={s}_{j}$, where $i+j=\tau $.

**Definition**

**2**

**[21].**Let $S=\left\{{s}_{i}|i=0,1,\dots ,2t\right\}$ be a linguistic term set, a PLTS can be defined as:

**Definition**

**3**

**[21].**Given a PLTS $L\left(p\right)=\left\{{L}^{\left(\mu \right)}\left({p}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#L\left(p\right)\right\}$, and ${r}^{\left(\mu \right)}$ is the subscript of the linguistic term ${L}^{\left(\mu \right)}$. $L\left(p\right)$ is an ordered PLTS if and only if the arrangement of linguistic terms ${L}^{\left(\mu \right)}\left({p}^{\left(\mu \right)}\right)\left(\mu =1,2,\dots ,\#L\left(p\right)\right)$ is organized in descending order based on the values of ${r}^{\left(\mu \right)}{p}^{\left(\mu \right)}\left(\mu =1,2,\dots ,\#L\left(p\right)\right)$.

**Definition**

**4**

**[21].**Given a PLTS $L\left(p\right)$ with $\sum _{\mu =1}^{\#L\left(p\right)}{p}^{\left(\mu \right)}<1$, then the normalized PLTS $\dot{L}\left(p\right)$ is defined by

**Definition**

**5**

**[26].**Let $S=\left\{{s}_{i}|i=0,1,\dots ,2t\right\}$ be a linguistic term set and $L\left(p\right)=\left\{{L}^{\left(\mu \right)}\left({p}^{\left(\mu \right)}\right)|{L}^{\left(\mu \right)}\in S,{p}^{\left(\mu \right)}\ge 0,\mu =1,2,\dots ,\#L\left(p\right),{\displaystyle \sum _{\mu =1}^{\#L\left(p\right)}{p}^{\left(\mu \right)}\le 1}\right\}$ be a PLTS, then the linguistic scale function is defined by

**Definition**

**6**

**[26].**Let $L\left(p\right)=\left\{{L}^{\left(\mu \right)}\left({p}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#L\left(p\right)\right\}$ be a PLTS and ${r}^{\left(\mu \right)}$ is the subscript of the linguistic term ${L}^{\left(\mu \right)}$. Then the score of $L\left(p\right)$ is defined as:

**Definition**

**7**

**[21].**Let ${L}_{1}\left(p\right)$ and ${L}_{2}\left(p\right)$ be two ordered PLTSs, ${L}_{1}\left(p\right)=\left\{{{L}_{1}}^{\left(\mu \right)}\left({{p}_{1}}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#{L}_{1}\left(p\right)\right\}$ and ${L}_{2}\left(p\right)=\left\{{{L}_{2}}^{\left(\mu \right)}\left({{p}_{2}}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#{L}_{2}\left(p\right)\right\}$ . Then:

- (1)
- ${L}_{1}\left(p\right)\oplus {L}_{2}\left(p\right)={\cup}_{{{L}_{1}}^{\left(\mu \right)}\in {L}_{1}\left(p\right),{{L}_{2}}^{\left(\mu \right)}\in {L}_{2}\left(p\right)}\left\{{{p}_{1}}^{\left(\mu \right)}{{L}_{1}}^{\left(\mu \right)}\oplus {{p}_{2}}^{\left(\mu \right)}{{L}_{2}}^{\left(\mu \right)}\right\};$
- (2)
- ${L}_{1}\left(p\right)\otimes {L}_{2}\left(p\right)={\cup}_{{{L}_{1}}^{\left(\mu \right)}\in {L}_{1}\left(p\right),{{L}_{2}}^{\left(\mu \right)}\in {L}_{2}\left(p\right)}\left\{{({{L}_{1}}^{\left(\mu \right)})}^{{{p}_{1}}^{\left(\mu \right)}}\otimes {({{L}_{2}}^{\left(\mu \right)})}^{{{p}_{2}}^{\left(\mu \right)}}\right\};$
- (3)
- $\lambda L\left(p\right)={\cup}_{{L}^{\left(\mu \right)}\in L\left(p\right)}\lambda {p}^{\left(\mu \right)}{L}^{\left(\mu \right)},\lambda \ge 0;$
- (4)
- ${\left(L\left(p\right)\right)}^{\lambda}={\cup}_{{L}^{\left(\mu \right)}\in L\left(p\right)}\left\{{\left({L}^{\left(\mu \right)}\right)}^{\lambda {p}^{\left(\mu \right)}}\right\}.$where ${{L}_{1}}^{\left(\mu \right)}$ and ${{L}_{2}}^{\left(\mu \right)}$ are the μth linguistic terms in ${L}_{1}\left(p\right)$ and ${L}_{2}\left(p\right)$, ${{p}_{1}}^{\left(\mu \right)}$ and ${{p}_{2}}^{\left(\mu \right)}$ are the probabilities of the μth linguistic terms in ${L}_{1}\left(p\right)$ and ${L}_{2}\left(p\right)$, respectively.

**Definition**

**8**

**[42].**Let $S=\left\{{s}_{i}|i=0,1,\dots ,2t\right\}$ be a linguistic term set and $\dot{L}\left(p\right)=\left\{{L}^{\left(\mu \right)}\left({\dot{p}}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#L\left(p\right)\right\}$ be a normalized PLTS, then its linguistic term vector $\alpha $ is denoted by

**Definition**

**9**

**[42].**Let $S=\left\{{s}_{i}|i=0,1,\dots ,2t\right\}$ be a linguistic term set, ${\dot{L}}_{1}\left(p\right)=\left\{{{L}_{1}}^{\left(\mu \right)}\left({{\dot{p}}_{1}}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#{L}_{1}\left(p\right)\right\}$ and ${\dot{L}}_{2}\left(p\right)=\left\{{{L}_{2}}^{\left(\mu \right)}\left({{\dot{p}}_{2}}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#{L}_{2}\left(p\right)\right\}$ be two normalized PLTSs, then the relative repetition degree δ and the diversity degree μ between ${\dot{L}}_{1}\left(p\right)$ and ${\dot{L}}_{2}\left(p\right)$ are computed by

**Definition**

**10**

**[42].**Let ${\dot{L}}_{1}\left(p\right)$ and ${\dot{L}}_{2}\left(p\right)$ be two normalized PLTSs, then the distance measure between them is defined as:

**Definition**

**11**

**[43].**Let $S=\left\{{s}_{i}|i=0,1,\dots ,2t\right\}$ be a linguistic term set, then the entropy of s

_{i}is computed by

**Definition**

**12**

**[44].**Let $L\left(p\right)=\left\{{L}^{\left(\mu \right)}\left({p}^{\left(\mu \right)}\right)|\mu =1,2,\dots ,\#L\left(p\right)\right\}$ be a PLTS, then the entropy of $L\left(p\right)$ can be calculated by

## 4. The Proposed CREAM

_{k}. Next, a step-by-step procedure of the proposed CREAM is explained.

_{kh}between expert E

_{k}and expert E

_{h}is calculated by

_{kh}is the trust degree of expert E

_{k}on expert E

_{h}obtained by the expert E

_{k}. Note that t

_{kh}satisfies $0\le {t}_{kh}\le 1$; ${t}_{kh}=1$ indicates that expert E

_{k}completely trusts expert E

_{h}, ${t}_{kh}=0$ means that expert E

_{k}does not trust expert E

_{h}at all.

_{k}of expert E

_{k}is computed by

_{k}of expert E

_{k}can be calculated by

_{k}of expert E

_{k}is computed by

_{k}, c

_{k}is the unit adjustment cost of expert E

_{k}, ${{L}^{\ast}}_{k}$ is the optimally adjusted state assessment matrix of expert E

_{k}. By solving model (19), the final adjusted state assessment matrix ${L}^{\ast}$ can be obtained.

_{0}and $\delta $ are calculated by the upper and lower bounds of the CPC effect indexes and HEP estimations. Based on the correspondence between control modes and the probability of action failure, it is appropriate to let $HE{P}_{\mathrm{min}}=0.00005$ and $HE{P}_{\mathrm{max}}=1.0$ [49]. In this study, $HE{P}_{0}=7.07\times {10}^{-3}$ and $\delta =-4.9517$, and thus the HEP for the task ${T}_{i}$ can be represented as:

## 5. Case Study

#### 5.1. Implementation and Results

_{1}, the probabilistic linguistic state assessment matrix ${L}_{1}={\left[{{L}_{ij}}^{1}\left(p\right)\right]}_{4\times 9}$ is obtained as shown in Table 2. The trust degree matrix of experts is exhibited as:

#### 5.2. Comparison Analysis

_{3}ranks third via the proposed CREAM, the HFM-CREAM, the ER-CREAM, and the modified CREAM. Besides, except for the CII-CREAM, the other four methods place T

_{4}in second place. Furthermore, the priority of tasks obtained by the proposed CREAM is identical to the results determined by the HFM-CREAM, the ER-CREAM, and the modified CREAM. These results imply the availability and practicality of the proposed CREAM.

_{1}is situated first by the proposed CREAM but is in the fourth position with the CII-CREAM. In addition, T

_{2}occupies the fourth position by using the proposed CREAM. But by the CII-CREAM, T

_{2}stands in the first place. These inconsistent results may be explained by the following points: First, the PLTSs are not used in the CII-CREAM, which cannot express the uncertain assessments of experts accurately and reflect the probabilistic information effectively. Second, the CPCs are treated as the same weight in the CII-CREAM, and the interactions between CPCs are not considered. Third, the CII-CREAM uses the performance influence index to quantify the overall impact of CPCs in solving HRA problems, which cannot realize the continuity of HEPs.

_{ik}is equal to 0 or 1 for all i and k. By solving the model above, the optimal ranking of four operational tasks in the case study is determined as ${T}_{1}>{T}_{4}>{T}_{3}>{T}_{2}$, which is identical to the ranking results calculated by the proposed CREAM model. Thus, the proposed CREAM model provides a more logical and credible HEP ranking in the specified application.

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AHP | Analytic hierarchy process |

ANP | Analytic network process |

CPCs | Common performance conditions |

CII-CREAM | Context influence index CREAM |

CREAM | Cognitive reliability and error analysis method |

DANP | Decision-making trial and evaluation laboratory-based analytic network process |

ER-CREAM | Evidential reasoning CREAM |

GRA | Grey relation analysis |

HEP | Human error probability |

HFM-CREAM | Hesitant fuzzy matrix CREAM |

HRA | Human reliability analysis |

MCCM | Minimum conflict consensus model |

PCR | Polymerase chain reaction |

PLTSs | Probabilistic linguistic term sets |

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CPCs | States | Effects |
---|---|---|

Proper organization (C_{1}) | s_{0}: Deficient | Reduced |

s_{1}: Inefficient | Reduced | |

s_{2}: Efficient | Not significant | |

s_{3}: Quite efficient | Improved | |

s_{4}: Very efficient | Improved | |

Working conditions (C_{2}) | s_{0}: Incompatible | Reduced |

s_{1}: Compatible | Not significant | |

s_{2}: Advantageous | Improved | |

Appropriate Man-machine Interface (MMI) and operational support (C_{3}) | s_{0}: Very inappropriate | Reduced |

s_{1}: Inappropriate | Reduced | |

s_{2}: Tolerable | Not significant | |

s_{3}: Adequate | Improved | |

s_{4}: Supportive | Improved | |

Available procedures and plans (C_{4}) | s_{0}: Inappropriate | Reduced |

s_{1}: Acceptable | Not significant | |

s_{2}: Appropriate | Improved | |

Number of simultaneous goals achieved (C_{5}) | s_{0}: Far beyond actual capacity | Reduced |

s_{1}: More than actual capacity | Reduced | |

s_{2}: Matching current capacity | Not significant | |

s_{3}: Fewer than actual capacity | Improved | |

s_{4}: Far fewer than actual capacity | Improved | |

Available time (C_{6}) | s_{0}: Continuously inadequate | Reduced |

s_{1}: Temporarily inadequate | Reduced | |

s_{2}: Adequate | Improved | |

Fatigue and distraction (C_{7}) | s_{0}: High | Reduced |

s_{1}: Acceptable | Not significant | |

s_{2}: Low | Improved | |

Adequate training and preparation (C_{8}) | s_{0}: Inadequate | Reduced |

s_{1}: Adequate, limited experience | Not significant | |

s_{2}: Adequate, high experience | Improved | |

Quality of crew collaboration (C_{9}) | s_{0}: Deficient | Reduced |

s_{1}: Inefficient | Reduced | |

s_{2}: Efficient | Not significant | |

s_{3}: Quite efficient | Improved | |

s_{4}: Very efficient | Improved |

Tasks | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} |
---|---|---|---|---|---|---|---|---|---|

T_{1} | {s_{1}(0.40), s_{2}(0.60)} | {s_{1}(0.50), s_{2}(0.50)} | {s_{2}(0.35), s_{4}(0.65)} | {s_{0}(0.50), s_{1}(0.50)} | {s_{0}(0.30), s_{1}(0.70)} | {s_{0}(0.40), s_{2}(0.60)} | {s_{1}(0.85), s_{2}(0.15)} | {s_{1}(0.80), s_{2}(0.20)} | {s_{1}(0.90), s_{2}(0.10)} |

T_{2} | {s_{3}(0.42), s_{4}(0.58)} | {s_{1}(0.38), s_{2}(0.62)} | {s_{1}(0.35), s_{2}(0.65)} | {s_{0}(0.23), s_{1}(0.77)} | {s_{2}(0.70), s_{4}(0.30)} | {s_{0}(0.68), s_{2}(0.32)} | {s_{1}(0.78), s_{2}(0.22)} | {s_{1}(0.80), s_{2}(0.20)} | {s_{3}(0.85), s_{4}(0.15)} |

T_{3} | {s_{1}(0.60), s_{2}(0.40)} | {s_{1}(0.55), s_{2}(0.45)} | {s_{1}(0.30), s_{2}(0.70)} | {s_{1}(0.40), s_{2}(0.60)} | {s_{0}(0.65), s_{2}(0.35)} | {s_{1}(0.80), s_{2}(0.20)} | {s_{1}(0.15), s_{2}(0.85)} | {s_{1}(0.20), s_{2}(0.80)} | {s_{1}(0.30), s_{2}(0.70)} |

T_{4} | {s_{1}(0.48), s_{2}(0.52)} | {s_{1}(0.30), s_{2}(0.70)} | {s_{1}(0.40), s_{2}(0.60)} | {s_{0}(0.70), s_{2}(0.30)} | {s_{2}(0.66), s_{4}(0.34)} | {s_{1}(0.30), s_{2}(0.70)} | {s_{1}(0.80), s_{2}(0.20)} | {s_{1}(0.75), s_{2}(0.25)} | {s_{1}(0.17), s_{2}(0.83)} |

Tasks | E_{1} | E_{2} | E_{3} | E_{4} | E_{5} |
---|---|---|---|---|---|

E_{1} | 0.000 | 0.011 | 0.004 | 0.000 | 0.012 |

E_{2} | 0.011 | 0.000 | 0.010 | 0.004 | 0.000 |

E_{3} | 0.004 | 0.010 | 0.000 | 0.003 | 0.019 |

E_{4} | 0.000 | 0.004 | 0.003 | 0.000 | 0.006 |

E_{5} | 0.012 | 0.000 | 0.019 | 0.006 | 0.000 |

Tasks | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} |
---|---|---|---|---|---|---|---|---|---|

T_{1} | {s_{1}(0.26), s_{2}(0.66), s_{3}(0.08)} | {s_{0}(0.04), s_{1}(0.61), s_{2}(0.35)} | {s_{0}(0.04), s_{2}(0.29), s_{4}(0.67)} | {s_{0}(0.28), s_{1}(0.69), s_{2}(0.03)} | {s_{0}(0.19), s_{1}(0.75), s_{2}(0.06)} | {s_{0}(0.34), s_{1}(0.05), s_{2}(0.61)} | {s_{1}(0.78), s_{2}(0.22)} | {s_{0}(0.10), s_{1}(0.80), s_{2}(0.10)} | {s_{1}(0.84), s_{2}(0.16)} |

T_{2} | {s_{3}(0.63), s_{4}(0.37)} | {s_{1}(0.31), s_{2}(0.69)} | {s_{1}(0.25), s_{2}(0.75)} | {s_{0}(0.21), s_{1}(0.79)} | {s_{2}(0.73), s_{3}(0.10), s_{4}(0.17)} | {s_{0}(0.59), s_{1}(0.06), s_{2}(0.35)} | {s_{1}(0.75), s_{2}(0.25)} | {s_{0}(0.10), s_{1}(0.78), s_{2}(0.12)} | {s_{3}(0.77), s_{4}(0.23)} |

T_{3} | {s_{1}(0.38), s_{2}(0.62)} | {s_{1}(0.68), s_{2}(0.32)} | {s_{1}(0.20), s_{2}(0.80)} | {s_{1}(0.45), s_{2}(0.55)} | {s_{0}(0.68), s_{2}(0.32)} | {s_{0}(0.03), s_{1}(0.76), s_{2}(0.21)} | {s_{1}(0.18), s_{2}(0.82)} | {s_{1}(0.29), s_{2}(0.71)} | {s_{1}(0.40), s_{2}(0.60)} |

T_{4} | {s_{1}(0.38), s_{2}(0.62)} | {s_{1}(0.31), s_{2}(0.69)} | {s_{1}(0.30), s_{2}(0.70)} | {s_{0}(0.73), s_{2}(0.27)} | {s_{2}(0.72), s_{3}(0.16), s_{4}(0.12)} | {s_{1}(0.32), s_{2}(0.68)} | {s_{1}(0.76), s_{2}(0.24)} | {s_{0}(0.11), s_{1}(0.74), s_{2}(0.15)} | {s_{1}(0.18), s_{2}(0.82)} |

Tasks | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} |
---|---|---|---|---|---|---|---|---|---|

T_{1} | {s_{1}(0.26), s_{2}(0.66), s_{3}(0.08)} | {s_{0}(0.04), s_{1}(0.61), s_{2}(0.35)} | {s_{0}(0.04), s_{2}(0.29), s_{4}(0.67)} | {s_{0}(0.28), s_{1}(0.69), s_{2}(0.03)} | {s_{0}(0.19), s_{1}(0.75), s_{2}(0.06)} | {s_{0}(0.34), s_{1}(0.05), s_{2}(0.61)} | {s_{1}(0.78), s_{2}(0.22)} | {s_{0}(0.10), s_{1}(0.80), s_{2}(0.10)} | {s_{1}(0.84), s_{2}(0.16)} |

T_{2} | {s_{3}(0.60), s_{4}(0.40)} | {s_{1}(0.31), s_{2}(0.69)} | {s_{1}(0.25), s_{2}(0.75)} | {s_{0}(0.21), s_{1}(0.79)} | {s_{2}(0.73), s_{3}(0.10), s_{4}(0.17)} | {s_{0}(0.59), s_{1}(0.06), s_{2}(0.35)} | {s_{1}(0.75), s_{2}(0.25)} | {s_{0}(0.10), s_{1}(0.78), s_{2}(0.12)} | {s_{3}(0.77), s_{4}(0.23)} |

T_{3} | {s_{1}(0.38), s_{2}(0.62)} | {s_{1}(0.66), s_{2}(0.34)} | {s_{1}(0.18), s_{2}(0.82)} | {s_{1}(0.47), s_{2}(0.53)} | {s_{0}(0.68), s_{2}(0.32)} | {s_{0}(0.03), s_{1}(0.76), s_{2}(0.21)} | {s_{1}(0.18), s_{2}(0.82)} | {s_{1}(0.29), s_{2}(0.71)} | {s_{1}(0.40), s_{2}(0.60)} |

T_{4} | {s_{1}(0.40), s_{2}(0.60)} | {s_{1}(0.31), s_{2}(0.69)} | {s_{1}(0.30), s_{2}(0.70)} | {s_{0}(0.73), s_{2}(0.27)} | {s_{2}(0.72), s_{3}(0.16), s_{4}(0.12)} | {s_{1}(0.33), s_{2}(0.67)} | {s_{1}(0.76), s_{2}(0.24)} | {s_{0}(0.11), s_{1}(0.74), s_{2}(0.15)} | {s_{1}(0.18), s_{2}(0.82)} |

Tasks | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} |
---|---|---|---|---|---|---|---|---|---|

T_{1} | 0.378 | 0.253 | 0.120 | 0.286 | 0.255 | 0.021 | 0.323 | 0.331 | 0.324 |

T_{2} | 0.184 | 0.128 | 0.387 | 0.327 | 0.333 | 0.025 | 0.311 | 0.323 | 0.236 |

T_{3} | 0.373 | 0.273 | 0.395 | 0.195 | 0.132 | 0.315 | 0.075 | 0.120 | 0.371 |

T_{4} | 0.371 | 0.128 | 0.382 | 0.000 | 0.347 | 0.137 | 0.315 | 0.306 | 0.395 |

Tasks | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} |
---|---|---|---|---|---|---|---|---|---|

T_{1} | 1.000 | 0.924 | 0.304 | 0.873 | 0.735 | 0.066 | 1.000 | 1.000 | 0.821 |

T_{2} | 0.488 | 0.470 | 0.981 | 1.000 | 0.959 | 0.079 | 0.962 | 0.975 | 0.599 |

T_{3} | 0.989 | 1.000 | 1.000 | 0.595 | 0.382 | 1.000 | 0.231 | 0.363 | 0.940 |

T_{4} | 0.983 | 0.470 | 0.967 | 0.000 | 1.000 | 0.434 | 0.974 | 0.925 | 1.000 |

Tasks | The Traditional CREAM | The CII-CREAM | The HFM-CREAM | The ER-CREAM | The Modified CREAM | The Proposed CREAM |
---|---|---|---|---|---|---|

Task 1 | 1 | 4 | 1 | 1 | 1 | 1 |

Task 2 | 1 | 1 | 4 | 4 | 4 | 4 |

Task3 | 1 | 2 | 3 | 3 | 3 | 3 |

Task 4 | 1 | 3 | 2 | 2 | 2 | 2 |

Tasks | Ranking | |||
---|---|---|---|---|

1 | 2 | 3 | 4 | |

T_{1} | 5 | 0 | 0 | 1 |

T_{2} | 1 | 1 | 0 | 4 |

T_{3} | 0 | 1 | 5 | 0 |

T_{4} | 0 | 4 | 1 | 1 |

Tasks | Ranking | |||
---|---|---|---|---|

1 | 2 | 3 | 4 | |

T_{1} | 5 | 5 | 5 | 6 |

T_{2} | 1 | 2 | 2 | 6 |

T_{3} | 0 | 1 | 6 | 6 |

T_{4} | 0 | 4 | 5 | 6 |

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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Xu, X.-G.; Zhang, L.; Wang, S.-X.; Gong, H.-P.; Liu, H.-C.
An Integrated CREAM for Human Reliability Analysis Based on Consensus Reaching Process under Probabilistic Linguistic Environment. *Systems* **2024**, *12*, 249.
https://doi.org/10.3390/systems12070249

**AMA Style**

Xu X-G, Zhang L, Wang S-X, Gong H-P, Liu H-C.
An Integrated CREAM for Human Reliability Analysis Based on Consensus Reaching Process under Probabilistic Linguistic Environment. *Systems*. 2024; 12(7):249.
https://doi.org/10.3390/systems12070249

**Chicago/Turabian Style**

Xu, Xue-Guo, Ling Zhang, Si-Xuan Wang, Hua-Ping Gong, and Hu-Chen Liu.
2024. "An Integrated CREAM for Human Reliability Analysis Based on Consensus Reaching Process under Probabilistic Linguistic Environment" *Systems* 12, no. 7: 249.
https://doi.org/10.3390/systems12070249