Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries

Jana, Dipak Kumar; Pramanik, Sutapa; Sahoo, Palash; Mukherjee, Anupam

doi:10.1007/s00500-017-2860-8

Cited by 35 publications

(16 citation statements)

References 35 publications

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“…Political articles focused on predicting presidential outcomes, a categorical target (Graefe, 2015, 2018; Graefe, Armstrong, Jones Jr, & Cuzán, 2014a, 2014b; Hurley & Lior, 2002; Morgan, 2014). Risk‐related targets were continuous and categorical: the probability of structural damage, nuclear fallout, occupational hazards, and balancing power load (Adams et al, 2009; Baecke et al, 2017; Brito et al, 2012; Brito & Griffiths, 2016; Cabello et al, 2012; Craig et al, 2001; Hathout et al, 2016; Jana et al, 2019; Kläs et al, 2010; Mu & Xianming, 1999; Neves & Frangopol, 2008; Ren‐jun & Xian‐zhong, 2002; Wang et al, 2008; Zio, 1996; Zio & Apostolakis, 1997). Public health papers predicted continuous targets over time, like forecasting carcinogenic risk (Evans et al, 1994) and US mortality rates (Alho, 1992).…”

Section: Resultsmentioning

confidence: 99%

“…Frequentist models for combining forecasts (Adams et al, 2009; Alho, 1992; Alvarado‐Valencia et al, 2017; Baecke et al, 2017; Baldwin, 2015; Borsuk, 2004; Cabello et al, 2012; Cooke et al, 2014; Evans et al, 1994; Franses, 2011; Graefe, Armstrong, Jones Jr, & Cuzán, 2014a; Gu et al, 2016; Hanea et al, 2018; Hathout et al, 2016; Hora & Kardeş, 2015; Hurley & Lior, 2002; Jana et al, 2019; Kläs et al, 2010; Kurowicka et al, 2010; Mak et al, 1996; Morales‐Nápoles et al, 2017; Mu & Xianming, 1999; Ren‐jun & Xian‐zhong, 2002; Seifert & Hadida, 2013; Shin et al, 2013; Wang et al, 2008) were typically convex combinations of expert judgment or linear regression models that included expert judgment as a covariate. Including expert judgment as a covariate in a linear regression model is related to judgmental bootstrapping (Armstrong, 2001b) and the Brunswik lens model (Hammond & Stewart, 2001).…”

Section: Resultsmentioning

confidence: 99%

“…These fields focus on decision makers and their ability to make predictions from data that cannot easily be collected and fed to a statistical model. Other areas of active research in expert opinion are quality assurance (Kläs et al, 2010), politics (Cai, Lin, Han, Liu, & Zhang, 2016; Graefe, 2015, 2018; Graefe, Armstrong, Jones Jr, & Cuzán, 2014a, 2014b; Hanea, McBride, Burgman, & Wintle, 2018; Satopää, Jensen, Mellers, Tetlock, & Ungar, 2014; Wang & Zhang, 2018), economics (Huang et al, 2016; Mak et al, 1996; Shin et al, 2013), engineering (Brito et al, 2012; Brito & Griffiths, 2016; Craig et al, 2001; Hathout, Vuillet, Peyras, Carvajal, & Diab, 2016; Jin, Lu, & Gan, 2007; Neves & Frangopol, 2008; Ren‐jun & Xian‐zhong, 2002; Tartakovsky, 2007; Wang, Du, & Cao, 2008; Zio, 1996), sports (Gu, Saaty, & Whitaker, 2016), sociology (Adams, White, & Ceylan, 2009; Cabello et al, 2012), meteorological (Abramson, Brown, Edwards, Murphy, & Winkler, 1996), ecological (Borsuk, 2004; Cooke et al, 2014; Failing et al, 2004; Johnson et al, 2018), environmental science (Li, Liu, & Yang, 2012; Mantyka‐Pringle, Martin, Moffatt, Linke, & Rhodes, 2014; Morales‐Nápoles et al, 2017; Zio & Apostolakis, 1997), and public health (Alho, 1992; Evans et al, 1994; Jana, Pramanik, Sahoo, & Mukherjee, 2019; Kurowicka, Bucura, Cooke, & Havelaar, 2010). The diversity and breadth of applications underscore the importance of expert opinion in a wide variety of disciplines.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

McAndrew

Wattanachit

Gibson

et al. 2020

WIREs Computational Stats

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Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts—models that combine expert‐generated predictions into a single forecast—can make predictions when training data is limited by relying on human intuition. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This review surveyed recent literature on aggregating expert‐elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Multivariate Analysis

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

McAndrew

Wattanachit

Gibson

et al. 2020

WIREs Computational Stats

View full text Add to dashboard Cite

show abstract

“…In many of the studies conducted on risk assessment, researchers have used MCDM methods as well as expert knowledge to assess the risks. Also, due to the uncertain nature of risk, fuzzy sets [1,2], interval-valued fuzzy sets [11,15], interval-valued intuitionistic fuzzy sets [16,17], and so on are used in many models to overcome this kind of uncertainty and ambiguity. On the other hand, in many real-world problems in the field of risk assessment, for various reasons such as differences in experience and knowledge level of experts, time pressures, etc., decision-makers do not usually come to a consensus on complex decisions on specific elements and getting an agreement is usually difficult.…”

Section: Introductionmentioning

confidence: 99%

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

Dahooie

Vanaki

Firoozfar

et al. 2020

IJERPH

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The construction industry is considered as one of the most dangerous industries in terms of occupational safety and has a high rate of occupational incidents and risks compared to other industries. Given the importance of identifying and assessing the occupational hazards in this industry, researchers have conducted numerous studies using statistical methods, multi-criteria decision-making methods, expert-based judgments, and so on. Although, these researchers have used linguistic variables, fuzzy sets and interval-valued intuitionistic fuzzy sets to overcome challenges such as uncertainty and ambiguity in the risk assessment conducted by experts; the previous models lack in efficiency if the experts are hesitant in their assessment. This leads to the inability to assign a specific membership degree to any risk. Therefore, in this research, it is tried to provide an improved approach to the Failure Mode and Effects Analysis (FMEA) method using an Multi-Criteria Decision-Making (MCDM) method based on the hesitant fuzzy set, which can effectively cope with the hesitance of the experts in the evaluation. Also, Stepwise Weight Assessment Ratio Analysis (SWARA) method is applied for risk factor weighing in the proposed approach. This model is applied to a construction industry case study to solve a realistic occupational risk assessment. Moreover, a comparison is made between the results of this model and those obtained by the conventional FMEA and some other aggregation operators. The results indicate that the newly developed approach is useful and flexible to address complex FMEA problems and can generate logical and reliable priority rankings for failure modes.

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“…Fuzzy logic methods were used to process the subjective assessment of experts. Other approaches based upon the use of fuzzy logic methods for the assessment of safety risks in the construction industry were presented in References [14,15]. Fuzzy logic methods were also used for assessing safety performance in the Indian construction industry [16] by predicting various types of accidents using a fuzzy inference engine.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Safety Systems for Machine Tools with Computer Numerical Control using Analytic Hierarchy Process

Racz

Breaz

Cioca

2019

Safety

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Computer numerical control (CNC) machine tools are complex production systems with fully automatic machine parts. Nowadays, high feed rates and machining speeds are used during the machining process. Human operators are still needed to set-up the machine, load/unload workpieces and parts, load the machining code, and supervise the machining process. The operators work in an environment where automated high-speed motions occur, and consequently, CNC machine tools have to be equipped with safety systems. The approach presented in this paper was to evaluate the main safety systems of CNC machine tools based upon the analytic hierarchy process (AHP). The analyzed systems were divided into six main categories and compared pairwise using five criteria proposed by the authors. The approach and the obtained results significantly relied upon the situation found at the industrial company used as a benchmark for the research. The analysis reveals that, among considered safety devices, manually operated controls are the most efficient ones. Finally, a sensitivity analysis was conducted to test the stability of the AHP solution.

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Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries

Cited by 35 publications

References 35 publications

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

Evaluating Safety Systems for Machine Tools with Computer Numerical Control using Analytic Hierarchy Process

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