A hybrid model integrating FMEA and HFACS to assess the risk of inter-city bus accidents

Liou, James J. H.; Liu, Perry C. Y.; Luo, Shiaw-Shyan; Lo, Huai-Wei; Wu, Yu-Zeng

doi:10.1007/s40747-022-00657-1

Cited by 19 publications

(11 citation statements)

References 52 publications

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“…Content and structural validity are often used to test the overall validity of a survey scale. Most of the items in the survey scale of unsafe behaviors and risk factors for deep coal miners developed in this study were adapted from a mature scale developed by domestic and foreign scholars [ 2 , 8 , 10 , 14 , 42 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. The self-compiled items were also modified several times based on literature analysis and expert consultation, combined with the actual production situation of deep coal mines.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

Yang

Wang

Zhu

et al. 2022

IJERPH

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The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners’ unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners’ unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners’ unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners’ unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners’ unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners’ unsafe behavior through the mediating effect of miners’ unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners’ unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization’s supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners’ unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines.

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Section: Methodsmentioning

confidence: 99%

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

Yang

Wang

Zhu

et al. 2022

IJERPH

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“…Pourmehdi et al ( 2021 ) used a combined approach of FANP (fuzzy analytic network process)-TOPSIS and FMEA to explore operational-related risks in the steel supply chain. There are also many common methodologies that are incorporated into FMEA, such as weighted aggregated sum product assessment (WASPAS) (Liou et al, 2022 ), hierarchical TOPSIS (Liu et al, 2018 ), interval type-2 fuzzy evidential reasoning method (Qin et al, 2020 ), Choquet integral method (Wang et al, 2019 ), and fuzzy Beta-Binomial approach (Wu & Wu, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…These shortcomings are as follows: (i) there are important risk factors not considered, such as expected cost ( E ); (ii) the same RPN value does not mean that the risk significance is the same; (iii) the RPN calculation is very sensitive to the risk variation; (iv) the weight of the risk factors should not be equal. Therefore, many researchers have devoted plenty of time to improving the suitability of FMEA, especially since the concept of multi-criteria decision analysis (MCDA) was incorporated (Başhan et al, 2020 ; Ghoushchi et al, 2019 ; Huang et al, 2019 ; Karatop et al, 2021 ; Liou et al, 2022 ; Liu et al, 2018 ; Lo et al, 2019 ; Pourmehdi et al, 2021 ; Qin et al, 2020 ; Wang et al, 2019 ; Wu & Wu, 2021 ; Yucesan et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

An FMEA model for risk assessment of university sustainability: using a combined ITARA with TOPSIS-AL approach based neutrosophic sets

Lin

2023

Ann Oper Res

Self Cite

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It is necessary to emphasize both the process and results of performance management to find the balance between quality and quantity needed to ensure the sustainable development of universities to make the best use of limited educational resources and meet the diverse needs of students. This study applies failure mode and effects analysis (FMEA) to analyze obstacles to university sustainability by constructing complete risk assessment modes and reference indicators. The neutrosophic set theory was incorporated into the FMEA to account for information uncertainty and asymmetry. A specialist team then evaluated the importance of the risk factors using neutrosophic indifference threshold-based attribute ratio analysis to determine objective weights for the risk factors. Furthermore, the neutrosophic technique for order preference by similarity to the ideal solution based on aspiration level (N-TOPSIS-AL) is employed to aggregate the total risk scores of the failure modes. Using neutrosophic sets to measure truth, falsity, and indeterminacy in the assessment significantly improve the adaptability of the fuzzy theory to real-world problems. The study results indicate that when assessing university affairs management and analyzing possible risks, priority must be given to the occurrence of risks and that the lack of educational facilities is the riskiest item in the specialist assessment. The proposed assessment model can be applied as a basis for university sustainability assessments to accelerate the development of other forward-looking approaches.

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“…In addition, the HFACS model of accident investigations is also an important qualitative approach to understanding and managing facility safety that is used by some government departments, such as the United States Coast Guard and the US Department of Defense [18]. To better understand the potential safe implications for avoiding the risks of picking and material-handling accidents in a warehouse facility [21], the HFACS model is used by this article and is described in detail as follows, including the five levels of human negligence [11,20,21]:…”

Section: Human Factors Analysis and Classification System (Hfacs)mentioning

confidence: 99%

Applications of BWM and GRA for Evaluating the Risk of Picking and Material-Handling Accidents in Warehouse Facilities

2023

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Warehouse accidents have become of wide concern to the public because they often cause heavy economic losses and heavy casualties. Therefore, it is critical to foster the safety and reliability of warehouse facilities to reduce accidents. However, in the relevant literature, the increased safety of warehouse facilities has seldom been researched. This is a main research gap that the authors would like to supplement with the aim of enhancing warehouse facility reliability to improve risk management in a comprehensive and reliable way. The objectives of this research article are to collect the critical risks from the relevant literature and interviewees, to calculate the weights of four critical indicators (O, S, D, and E) by the BWM approach, and to rank the critical risks of picking and material-handling accidents in a warehouse facility through GRA, the HFACS model, and the FMEA approach. The ranking results show that cost is the most essential element and the expected cost > detection > severity > occurrence, according to the average value of experts’ comprehensive consideration of importance. Next, GRA is used to prioritize the critical risks of picking and material-handling accidents. The main contribution is that we identify 18 major critical risk accidents of the HFACS risk categories and make up for the insufficiency of past research that lacks an empirical analysis of the risks of picking and material-handling accidents in warehouse facilities.

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A hybrid model integrating FMEA and HFACS to assess the risk of inter-city bus accidents

Cited by 19 publications

References 52 publications

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

An FMEA model for risk assessment of university sustainability: using a combined ITARA with TOPSIS-AL approach based neutrosophic sets

Applications of BWM and GRA for Evaluating the Risk of Picking and Material-Handling Accidents in Warehouse Facilities

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