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

Integrated multi-criteria group decision-making methods based on q-rung picture fuzzy sets for the identification of occupational hazards

Practical group decision-making (DM) problems frequently involve challenging circumstances when attempting to assign appropriate values to the data because of the haziness and uncertainty of the surrounding circumstances. In order to address the ambiguity and imprecision that arise in DM issues, q-rung picture fuzzy sets (q-RPFSs) have a more broader structure. In this research, the criteria importance through intercriteria correlation (CRITIC) and the decision-making trial and evaluation laboratory (DEMATEL) techniques are separately integrated with the multi-attributive border approximation area comparison (MABAC) method. The MABAC method, which measures how far each alternative is from the border approximation area, is very stable and useful for resolving real-world problems. The CRITIC technique calculates the criteria weights by taking into account the relationships between attributes, and the DEMATEL methodology is recognized as the best method for determining how several criteria or factors interact with one another. As a result of these justifications, we made the decision to create the CRITIC-MABAC and DEMATEL-MABAC procedures for q-RPFSs. By using the suggested strategies, the primary goal of this article is to determine the occupational risk that has the greatest impact on the health of a hospital's medical staff. We begin by employing the CRITIC technique to determine the criteria weights. In addition, we calculate the weights of the criteria using the DEMATEL approach. The offered methodologies are investigated for their applicability to determine the most serious occupational hazard for hospital employees. We conducted a comparison with three earlier studies to verify the accuracy of the tactics that are offered.

The relationship among safety leadership, risk perception, safety culture, and safety performance: Military volunteer soldiers as a case study

Safety is fundamental to any organization; if not based on safety, organizational decision-making and management would be meaningless. For a country, soldiers are responsible for national security; they serve as a barrier that defends a country against external invasive forces, thus assuming great missions and responsibilities on their shoulders. To ensure soldiers fulfill their duties of protecting the country and the people, they should gain clear risk perception, which should be instilled into them during their daily combat readiness training. Only when their performances meet safety criteria can they become a strong fighting force. This study recruited military volunteer soldiers as its research participants and employed convenience sampling to distribute questionnaires. In total, this research collected 725 valid copies, of which the data were used to explore the relationship among safety leadership, risk perception, safety culture, and safety performance. To achieve this goal, this study proposed some research hypotheses based on literature review. The hypotheses were all verified via latent variable modeling and multiple hierarchical regression analysis after the reliability and validity of each construct had been tested via confirmatory factor analysis. The research results showed that the more deeply military volunteer soldiers sense safety leadership, the clearer their risk perception will be and the more helpful it would be in achieving safety performance. It is worth mentioning that risk perception can serve as a mediator while safety culture can mediate the relationship between safety leadership and safety performance. Lastly, the research proposes suggestions in the section of conclusions, which provides reference to the combat readiness training and daily tasks of soldiers.

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

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.

Occupational Health and Safety Risk Assessment based on Combination Weighting and Uncertain Linguistic Information: Method Development and Application to a Construction Project

OCCUPATIONAL APPLICATIONSOccupational hazards and work-related accidents are a substantial problem in countries around the world. Therefore, it is of great importance to develop appropriate techniques to assess and reduce the risk of occupational hazards. In many situations, however, exact data are inadequate to model real-life scenarios, because of the complexity of occupational health and safety (OHS) risk assessment problems. We present a new OHS risk assessment model to assess and rank the risk of occupational hazards based on combination weighting and uncertain linguistic information. Moreover, a practical example of a shopping mall construction project is given to illustrate the effectiveness of the proposed model. The new model was found to provide a useful, practical, and flexible way for risk evaluation in OHS. In particular, it offered a new method for capturing domain expert opinions and prioritizing potential occupational hazards to improve the health and safety of workers.

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

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.

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

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.