Optimal selection of safety recommendations: A hybrid fuzzy multi-criteria decision-making approach to HAZOP

One of the most important outputs of Hazard and Operability (HAZOP) study is a list of recommendations (safety measures) for improving the safety of the system under study. Although, it is generally assumed that all the recommendations should be implemented, due to the limited resources (budget, time, etc.) simultaneous implementation of all the recommendations is not feasible in reality. The large number of recommendations, a wide range of factors which should be evaluated by experts, and the uncertainty in the expert opinions make the conventional methods ineffective. Despite the widespread use of HAZOP, the decision as to which recommendations to implement is still a challenge for process safety managers. The present study aims to develop (i) a comprehensive hierarchy of factors for the evaluation of the recommendations, (ii) a novel methodology by integrating the Best Worst Method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking the recommendations and (iii) fuzzy logic and group decision-making are used to consider the imprecision of expert opinions so as to improve the accuracy of the results. The proposed model is capable of considering most factors of recommendations for any number of HAZOP recommendations. The methodology can be applied to a broad range of hazardous facilities and industries. In the present study, however, its application has been demonstrated for a sulfuric acid storage system.

Automated summary

The Hazard and Operability (HAZOP) study generates a list of recommendations for system safety improvement, but due to resource limitations, it is necessary to prioritize and implement specific recommendations based on expert evaluation. This study aims to develop a comprehensive hierarchy of factors for recommendation evaluation, utilizing the Best Worst Method (BWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the recommendations and improve accuracy.