Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery

Knowledge mapping analysis of safety ergonomics: a bibliometric study

Although a significant attention, the field of safety ergonomics has not yet been systematically profiled based on recent studies. To fully understand the current research status, basis, hotspots, and development trends in the field, 533 documents from the Web of Science core database were used for knowledge mapping analysis by the bibliometric method. The study found that the USA is the top country in publications, and Tehran University is the institution with the highest number of publications. Ergonomics and Applied Economics are the authoritative safety ergonomics journals. Through co-occurrence and co-citation analysis, current safety ergonomics research is focussed on healthcare, product design, and occupational health and safety. The keyword timeline view indicates that the main research paths are occupational health and safety, and patient safety research. The analysis of burst keywords shows that safety ergonomics research in management, model design, and system design areas are research frontiers in the field.Practitioner summary: This paper presents a knowledge mapping of safety ergonomics research through bibliometric analysis. The research results show the research status, research hotspots, and research frontiers in the field of safety ergonomics, which provides a direction for other scholars to quickly understand the development of this field.

An integrated approach for evaluating and improving the performance of hospital ICUs based on ergonomic and work-motivational factors

The intensive care unit (ICU) holds significant importance in hospitals. Primarily concerned with monitoring and providing care to critically ill patients, the ICU has proven effective in reducing mortality rates and minimizing complications of diseases, thanks to the highly complex and specific measures taken within this department. Considering the unique contributions made by the staff in this unit, its performance assessment can help improve patient care and satisfaction. This study presents a framework that utilizes ergonomic and work-motivational factors (WMFs) to assess the performance of various ICUs. Upon the identification of these indicators, a standard questionnaire is developed to collect the required data. The mean efficiency score of the units is then determined using the data envelopment analysis (DEA). The model is validated using the principal component analysis (PCA). Ultimately, the SWOT (strengths, weaknesses, opportunities, and threats) matrix is employed to formulate an appropriate strategy and offer improvement measures to the managerial team to enhance their ICU performance. The proposed framework can be applied to evaluate the performance of other healthcare departments. Among the studied ICU centers, including general ICU, isolation ICU catering to individuals with infectious diseases, cardiac care unit (CCU), and neonatal ICU (NICU). NICU and general ICU have the best and worst performance in terms of macro- and micro-ergonomic and motivational indicators, which are on average 0.826% more elevated and 0.659% lower, respectively. According to the performed sensitivity analysis, the ICUs in question demonstrate the most appropriate and inappropriate performance about the indicators of "knowledge, situation assessment, and situation analysis" and "work stress", respectively.

Soft computing applications in the field of human factors and ergonomics: A review of the past decade of research

The main objectives of this study were to 1) review the literature on the applications of soft computing concepts to the field of human factors and ergonomics (HFE) between 2013 and 2022 and 2) highlight future developments and trends. Multiple soft computing methods and techniques have been investigated for their ability to address various applications in HFE effectively. These techniques include fuzzy logic, artificial neural networks, genetic algorithms, and their combinations. Applications of these methods in HFE have been highlighted in one hundred and four articles selected from 406 papers. The results of this study help address the challenges of complexity, vagueness, and imprecision in human factors and ergonomics research through the application of soft computing methodologies.

An integrative fuzzy Delphi Decision Making Trial and Evaluation Laboratory (DEMATEL) study on the risk perception influencing factors

PURPOSE

Risk perception is one of the factors that can guide human behavior in the workplace. The resent study aimed to identify the most important factors affecting risk perception.

MATERIALS AND METHODS

This cross-sectional study had three phases. In the first, factors affecting risk perception were extracted based on librarian resources and studies. Then, using the fuzzy-Delphi method and based on the expert's opinion, the most important factors affecting risk perception were identified. Finally, using the fuzzy-DEMATEL technique and relying on the opinions of experts, the relationships between these factors were analyzed and their role as cause or effect was determined.

RESULTS

Eleven variables were identified as the most important factors influencing risk perception, among which safety knowledge, quality, and quantity of safety training, and safety climate had the greatest effect on risk perception with defuzzified scores of 0.826, 0.817, and 0.808, respectively. Seven of the variables had the role of cause and four of them had the role of effect. Experience of Accident along with safety climate had the highest interaction with other variables.

CONCLUSION

Precise investigation of risk perception influencing factors can play an important role in improving risk perception and safe behaviors.

Human Factor Index Measurement Using an ISM-SEM-Fuzzy Approach

In recent years, there has been a trend toward automation and data exchange in manufacturing processes through industrial cognitive computing, the Internet of Things (IoT), and artificial intelligence. However, the human–machine interface plays a role in establishing a smart manufacturing system in any industry. It is necessary to develop a comprehensive model to identify the risk factors that contribute to the loss of human performance and productivity and evaluate the workplace for its compliance and agility toward safe human–machine systems. In this study, a model is proposed that can be used as a measurement tool to design ergonomic workplaces in the automotive industry. Several criteria have been classified under four enablers: physiological factors, psychological factors, environmental factors, and safety factors. These were identified through a literature review. The proposed model integrates the applications of structural equation modeling (SEM), interpretive structural modeling (ISM), and the multigrade fuzzy approach. ISM was employed to demonstrate the applicability of the model to depict various ergonomic enablers considered in the ergonomic measurement. SEM was used to validate the ergonomic measurement model statistically. Physiological factors were found to be highly correlated with ergonomic practices. Physiological and psychological factors were also highly correlated. The use of the multigrade fuzzy approach was demonstrated to determine the human factor index for an automotive component manufacturing industry. The proposed model can enable management to evaluate the various risk factors that hamper the ergonomic level of a company and thereby allow the company to harness the benefits of ergonomics to enhance safety and productivity.

Prioritization of leading operational indicators in occupational safety and health

New approaches are necessary to ensure the effectiveness of an Occupational Safety and Health Management System (OSHMS), which include the development of new methods that would facilitate the measurement of the proactive operational status. This study addresses the development of a tool that contains leading operational indicators in Occupational Safety and Health (OSH), with the main objective of prioritizing these indicators according to the consensual opinion of groups of experts. The Delphi method was applied to this manageable list to prioritize the indicators through three rounds, and a consensus was obtained on which indicators are most relevant, both for each individual group of experts and for all experts. This tool allows each company to customize the indicators that are most appropriate for its own reality by considering the report of these indicators. This tool can be a part of a safety dashboard model and considers only the most relevant indicators.

Fuzzy Logic Modeling of Factors Affecting Musculoskeletal Disorders in a Steel Factory in Iran: A Cross-Sectional Study

OBJECTIVES

The aim of this study was to model associated factors affecting musculoskeletal disorders (MSDs), using fuzzy logic in a steel factory in Iran.

METHODS

A cross-sectional study was conducted on steel industry workers. A 6-part questionnaire was used, consisting of demographic characteristics, occupational stress, work-family conflict, general health, occupational postures, and Nordic Musculoskeletal Questionnaire. Pearson correlation was used for statistical analysis.

RESULTS

The prevalence of MSDs for 270 participants in the studied factory was 94.8%. Job stress, work-family conflict, general health, and work posture had a statistically significant relationship with MSDs (P < .05). The fuzzy model demonstrated 23.8% predictability for the actual data of the study. The defuzzification data had significant correlation with real data of MSDs.

CONCLUSIONS

The results of this study provided a new perspective about associated factors affecting MSDs and demonstrate that fuzzy logic can be used as a possible tool for evaluating MSDs.

Fuzzy Based Risk Assessment for Decommissioning Concrete Bioshield Structures in Nuclear Power Plants: Structural Risks and Worker Safety

Few studies have assessed the safety issues involved in decommissioning nuclear facilities, especially from a structural and job perspective; in most developed countries, the focus is generally on the radiological risks. This study highlights the inadequacy of existing deterministic risk assessment methods, which cannot account for the uncertainty and complexity of hazards that workers are exposed to. We instead propose a fuzzy logic based safety assessment model that can analyze and compare alternatives utilizing a step-by-step risk quantification and multidimensional approach. This enables personnel to assess the various risks involved when decontaminating and decommissioning nuclear power plant structures that cannot be quantitatively assessed owing to a lack of data. Our proposed fuzzy based risk assessment model can also be applied to risk assessment in other engineering fields that depend on the judgment of experts supported by little or no statistical data.

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using different parameters. We have conducted a systematic mapping study based upon 70 systematically selected high quality peer reviewed research articles involving ES for earthquake prediction, published between January 2010 and January 2020.To the best of our knowledge, there is no recent study that provides a comprehensive survey of this research area. The analysis shows that most of the proposed models have attempted long term predictions about time, intensity, and location of future earthquakes. The article discusses different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction. Moreover, the discussion covers regional and global seismic data sets used, tools employed, to predict earth quake for different geographical regions. Bibliometric and meta-information based analysis has been performed by classifying the articles according to research type, empirical type, approach, target area, and system specific parameters. Lastly, it also presents a taxonomy of earthquake prediction approaches, and research evolution during the last decade.

A practical review and taxonomy of fuzzy expert systems: methods and applications

Purpose

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

Design/methodology/approach

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

Findings

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Originality/value

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

A fuzzy association rule-based knowledge management system for occupational safety and health programs in cold storage facilities

Purpose

This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment.

Design/methodology/approach

A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system.

Findings

The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity.

Practical implications

Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society.

Originality/value

This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

Health, safety and environmental unit performance assessment model under uncertainty (case study: steel industry)

Performance assessment is a critical objective of management systems. As a result of the non-deterministic and qualitative nature of performance indicators, assessments are likely to be influenced by evaluators' personal judgments. Furthermore, in developing countries, performance assessments by the Health, Safety and Environment (HSE) department are based solely on the number of accidents. A questionnaire is used to conduct the study in one of the largest steel production companies in Iran. With respect to health, safety, and environment, the results revealed that control of disease, fire hazards, and air pollution are of paramount importance, with coefficients of 0.057, 0.062, and 0.054, respectively. Furthermore, health and environment indicators were found to be the most common causes of poor performance. Finally, it was shown that HSE management systems can affect the majority of performance safety indicators in the short run, whereas health and environment indicators require longer periods of time. The objective of this study is to present an HSE-MS unit performance assessment model in steel industries. Moreover, we seek to answer the following question: what are the factors that affect HSE unit system in the steel industry? Also, for each factor, the extent of impact on the performance of the HSE management system in the organization is determined.

An integrated experiment for identification of best decision styles and teamworks with respect to HSE and ergonomics program: The case of a large oil refinery

Decision making failure is a predominant human error in emergency situations. To demonstrate the subject model, operators of an oil refinery were asked to answer a health, safety and environment HSE-decision styles (DS) questionnaire. In order to achieve this purpose, qualitative indicators in HSE and ergonomics domain have been collected. Decision styles, related to the questions, have been selected based on Driver taxonomy of human decision making approach. Teamwork efficiency has been assessed based on different decision style combinations. The efficiency has been ranked based on HSE performance. Results revealed that efficient decision styles resulted from data envelopment analysis (DEA) optimization model is consistent with the plant's dominant styles. Therefore, improvement in system performance could be achieved using the best operator for critical posts or in team arrangements. This is the first study that identifies the best decision styles with respect to HSE and ergonomics factors.

An Efficient Taguchi Approach for the Performance Optimization of Health, Safety, Environment and Ergonomics in Generation Companies

Background

A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented.

Methods

To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data.

Results

The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs.

Conclusion

The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.

Real-time safety risk assessment based on a real-time location system for hydropower construction sites

The concern for workers' safety in construction industry is reflected in many studies focusing on static safety risk identification and assessment. However, studies on real-time safety risk assessment aimed at reducing uncertainty and supporting quick response are rare. A method for real-time safety risk assessment (RTSRA) to implement a dynamic evaluation of worker safety states on construction site has been proposed in this paper. The method provides construction managers who are in charge of safety with more abundant information to reduce the uncertainty of the site. A quantitative calculation formula, integrating the influence of static and dynamic hazards and that of safety supervisors, is established to link the safety risk of workers with the locations of on-site assets. By employing the hidden Markov model (HMM), the RTSRA provides a mechanism for processing location data provided by the real-time location system (RTLS) and analyzing the probability distributions of different states in terms of false positives and negatives. Simulation analysis demonstrated the logic of the proposed method and how it works. Application case shows that the proposed RTSRA is both feasible and effective in managing construction project safety concerns.

Development of a decision support system for analysis and solutions of prolonged standing in the workplace

Background

Prolonged standing has been hypothesized as a vital contributor to discomfort and muscle fatigue in the workplace. The objective of this study was to develop a decision support system that could provide systematic analysis and solutions to minimize the discomfort and muscle fatigue associated with prolonged standing.

Methods

The integration of object-oriented programming and a Model Oriented Simultaneous Engineering System were used to design the architecture of the decision support system.

Results

Validation of the decision support system was carried out in two manufacturing companies. The validation process showed that the decision support system produced reliable results.

Conclusion

The decision support system is a reliable advisory tool for providing analysis and solutions to problems related to the discomfort and muscle fatigue associated with prolonged standing. Further testing of the decision support system is suggested before it is used commercially.