Fuzzy Human Reliability Analysis: Applications and Contributions Review

Baziuk, Pedro A.; Rivera, Selva S.; Leod, Jorge Núñez Mc

doi:10.1155/2016/4612086

“…The reliance on user judgments may decrease the degree of accuracy. Hence, approaches with less dependency on user judgment, such as probabilistic cognitive simulator, Bayesian networks (Kim et al, 2006;Shirley, Smidts, and Zhao, 2019;Zhou, et al, 2018), and Fuzzy logic methods (Baziuk, Rivera, and Leod, 2016;Kumar, Gupta, and Gunda, 2020;Swaanika, Sujatha, and Nagarajan, 2019), have been proposed to amend the model's reliance on user's judgment, which can improve the accuracy of the model's outcome (Bye, et al, 2006;Ekanem, Mosleh, and Shen, 2016;Giardina et al, 2019;Laumann, and Rasmussen, 2016;Li et al, 2012;Rasmussen, Standal, and Laumann, 2015). A Bayesian Belief Network (BBN) which is a possible probabilistic method, uses available data to define a relationship between task design risk factors and human-system error to quantify the probability of errors without any dependency on user's judgment (Aalipour et al, 2016;Groth and Mosleh, 2010;Mkrtchyan, Podofillini, and Dang, 2015;Quintana, and Leung, 2012).…”

Section: Discussionmentioning

confidence: 99%

The Integration of Human Factors to Warehousing and Manual Order Picking Operations

Setayesh

2023

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0

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<p>Human Factors (HF) identified as the main concern of studies since poor HF and system design interaction has a negative impact on quality, error occurrence, system performance, and worker’s well-being. This dissertation takes literature review and sensitivity analysis to assess human- system errors by focusing on: 1) finding the most suitable model to estimate human-system errors, 2) identifying failure modes causing human-system errors, 3) creating a tool that capable of identifying the source of pick errors and degraded quality at the initial stage of a design, 4) evaluating the usability, functionality, and usefulness of the developed tool. The most common Human Reliability Assessment (HRA) models were studied to identify the most suitable model to estimate human error probability (HEP) in various contexts. The qualitative and quantitative analysis among the most common HRA models showed that these models are suffering from lack of guideline on Performance Indicating Factors (PIFs) selection and multiplier values allocation. Hence, a need for creating and validating new empirically based models in different sectors were recognized. Therefore, the focus of the research study was changed from finding a suitable model to determining the source of errors in manual order picking operation. The developed Warehouse Design Error Prevention (WDEP) checklist tool integrated HF aspects to both Order Picking (OP) design elements and warehouse management elements based on the identified failure modes from literature and field studies as elaborated in chapter 3 of this dissertation. Tool usability, functionality, and usefulness were evaluated through the survey study and qualitative interviews. The outcome of the user evaluation found the created tool useful, functional, and useable both at the initial stage of a design as well as during process improvement process. These dissertation findings illustrate the need for developing HF measures that contribute to pick errors and optimize OP system design strategies concerning HF demands by creating and validating the error prevention checklist tool. </p>

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“…The reliance on user judgments may decrease the degree of accuracy. Hence, approaches with less dependency on user judgment, such as probabilistic cognitive simulator, Bayesian networks (Kim et al, 2006;Shirley, Smidts, and Zhao, 2019;Zhou, et al, 2018), and Fuzzy logic methods (Baziuk, Rivera, and Leod, 2016;Kumar, Gupta, and Gunda, 2020;Swaanika, Sujatha, and Nagarajan, 2019), have been proposed to amend the model's reliance on user's judgment, which can improve the accuracy of the model's outcome (Bye, et al, 2006;Ekanem, Mosleh, and Shen, 2016;Giardina et al, 2019;Laumann, and Rasmussen, 2016;Li et al, 2012;Rasmussen, Standal, and Laumann, 2015). A Bayesian Belief Network (BBN) which is a possible probabilistic method, uses available data to define a relationship between task design risk factors and human-system error to quantify the probability of errors without any dependency on user's judgment (Aalipour et al, 2016;Groth and Mosleh, 2010;Mkrtchyan, Podofillini, and Dang, 2015;Quintana, and Leung, 2012).…”

Section: Discussionmentioning

confidence: 99%

The Integration of Human Factors to Warehousing and Manual Order Picking Operations

Setayesh

2023

Preprint

0

View full text Add to dashboard Cite

<p>Human Factors (HF) identified as the main concern of studies since poor HF and system design interaction has a negative impact on quality, error occurrence, system performance, and worker’s well-being. This dissertation takes literature review and sensitivity analysis to assess human- system errors by focusing on: 1) finding the most suitable model to estimate human-system errors, 2) identifying failure modes causing human-system errors, 3) creating a tool that capable of identifying the source of pick errors and degraded quality at the initial stage of a design, 4) evaluating the usability, functionality, and usefulness of the developed tool. The most common Human Reliability Assessment (HRA) models were studied to identify the most suitable model to estimate human error probability (HEP) in various contexts. The qualitative and quantitative analysis among the most common HRA models showed that these models are suffering from lack of guideline on Performance Indicating Factors (PIFs) selection and multiplier values allocation. Hence, a need for creating and validating new empirically based models in different sectors were recognized. Therefore, the focus of the research study was changed from finding a suitable model to determining the source of errors in manual order picking operation. The developed Warehouse Design Error Prevention (WDEP) checklist tool integrated HF aspects to both Order Picking (OP) design elements and warehouse management elements based on the identified failure modes from literature and field studies as elaborated in chapter 3 of this dissertation. Tool usability, functionality, and usefulness were evaluated through the survey study and qualitative interviews. The outcome of the user evaluation found the created tool useful, functional, and useable both at the initial stage of a design as well as during process improvement process. These dissertation findings illustrate the need for developing HF measures that contribute to pick errors and optimize OP system design strategies concerning HF demands by creating and validating the error prevention checklist tool. </p>

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“…The information provided is characterized by ambiguity, incompleteness and other uncertainties [12,13]. Fuzzy set theory plays an important role in human reliability analysis [14]. In many references quoting CREAM, fuzzy logic is well applied to deal with uncertain information [8,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

Wu

¹

,

Xu

²

,

Wang

³

et al. 2021

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Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

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Fuzzy Human Reliability Analysis: Applications and Contributions Review

Cited by 17 publications

References 49 publications

The Integration of Human Factors to Warehousing and Manual Order Picking Operations

The Integration of Human Factors to Warehousing and Manual Order Picking Operations

The Integration of Human Factors to Warehousing and Manual Order Picking Operations

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

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