A hybrid risk prioritization method based on generalized TODIM and BWM for Fine-Kinney under interval type-2 fuzzy environment

The Fine − Kinney is a risk assessment method widely used in many industries due to its ease of use and quantitative risk evaluation. As in other methods, it is a method that recommends taking a series of control measures for operational safety. However, it is not always possible to implement control measures based on the determined priorities of the risks. It is considered that determining the priorities of these measures depends on many criteria such as applicability, functionality, performance, and integrity. Therefore, this study has studied the prioritization of control measures in Fine − Kinney-based risk assessment. The criteria affecting the prioritization of control measures are hierarchically structured, and the importance weights of the criteria are determined by the Bayesian Best–Worst Method (BBWM). The priorities of control measures were determined with the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) method. The proposed model has been applied to the risk assessment process in a petrol station’s liquid fuel tank area. According to the results obtained with BBWM, the most important criterion affecting the prioritization of control measures is the applicability criterion. It has an importance weight of about 42%. It is followed by performance with 31%, functionality with 18%, and integrity with 10%, respectively. FVIKOR results show that the “Periodic control of the ventilation device” measure is the top priority for Fine − Kinney risk assessment. “The absence of any ducts or sewer pits that may cause gas accumulation in the tank area and near the dispenser; Yellow line marking of entry and exit and vehicle roads; Placing of speed limit warning signs” has been determined as a secondary priority. On conclusion, this proposed model is expected to bring a new perspective to the work of occupational health and safety analysts, since the priority suggested by Fine − Kinney risk analysis methods is not always in the same order as the one in the stage of taking action, and the source, budget, and cost/benefit ratio of the measure affect this situation in practice. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-19454-x.

Section: Literature Reviewmentioning

confidence: 99%

Control measure prioritization in Fine − Kinney-based risk assessment: a Bayesian BWM-Fuzzy VIKOR combined approach in an oil station

Gül

Yücesan

2022

“…By combining the two methods, one can prioritize and categorize hazards, assess their risks, and make important decision-making recommendations to prevent risks from occurring. Tang et al ( 2021 ) aimed to improve a hybrid risk prioritization approach for Fine–Kinney using the generalized TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making), best–worst and interval type-2 fuzzy set. Dagsuyu et al ( 2020 ) developed a new approach to the classical Fine–Kinney risk assessment method.…”

Section: Literature Reviewmentioning

confidence: 99%

Action selection in risk assessment with fuzzy Fine–Kinney-based AHP-TOPSIS approach: a case study in gas plant

Dogan

Oturakci

Dağsuyu

2022

In this study, the hazards occurring in a medium-sized gas filling facility were defined, and the risk scores of these hazards were determined by the expert team according to the Fine–Kinney risk analysis method. However, since the same risk significance score is obtained in different combinations of scale values in the classical Fine–Kinney risk analysis method and the characteristics/constraints of the company applied in the risk analysis are not taken into account, the hazards were evaluated using fuzzy Fine–Kinney risk analysis, and the most critical hazards were determined. Action plans are defined for critical hazards determined as a result of fuzzy Fine–Kinney risk analysis. Among the actions that require company resources, the action selection was performed with the TOPSIS method, taking into account their relationship with the hazards by integrating the weights, which was calculated with the AHP method, of affected groups. The effect of operating constraints is included in the last step of the study to calculate the final weights. Calculating the results by including the risk-affected groups and company constraints and ranking the actions reveals that the study is an original, objective, and applicable study.

“…Komatina et al ( 2021 ) proposed two new parameters to PFMEA, and their weights were calculated with type-2 fuzzy BWM. Gölcük ( 2020 ) IT2F-BWM with perceptual reasoning by Tang et al ( 2021 ) TODIM and Celik and Gul ( 2021 ) integrated with MARCOS and Gölcük ( 2021 ) with WASPAS. In addition to risk assessment, type-2 BWM was used in particular areas.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

Baskak

Ozbey

Yücesan

et al. 2022

The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the “COVID-19 safe campus” evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers’ evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-22796-1.