Risk-based techniques such as risk graph and Layer of Protection Analysis (LOPA) are used to determine the Safety Integrity Level (SIL) of safety instrumented functions to ensure that risk is reduced to a tolerable level. However, these techniques have some drawbacks. For instance, they need absolute and precise numbers to evaluate SIL parameters, which are rarely available or are highly uncertain. In addition, they are incapable of considering individual and societal risks simultaneously. Moreover, risk tolerance criteria are likely to be used incorrectly in the LOPA technique, and risk graph is difficult to calibrate. In the current paper, a novel comprehensive fuzzy arithmetic model has been developed to determine the required SILs in process industries. The fuzzy required Risk Reduction Factor (RRF) is calculated for both individual and societal risks. Fuzzy numbers are developed from crisp intervals, based on the expected interval of the fuzzy numbers. Expert fuzzy-scaled elicitation has been applied to obtain the SIL parameters. In the proposed model, the overall risk tolerance criterion and apportionment factor are defined as SIL parameters for both individual and societal risks to ensure that the applied risk criteria are compliant with the requirements of the system. In addition, an approach is introduced for determining the required SIL based on the fuzzy required RRF. The proposed methodology was demonstrated to alleviate the limitations, and thus, can be considered as a more precise alternative to the conventional methods.

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Complex networks play a vital role in reliability analysis of real-world applications, demanding for precise and accurate analysis methods for optimal allocations of cost and reliability. Since the configuration of a system may change with every feasible solution of cost allocation optimization equation, finding the best arrangement of the system can become very challenging. This paper presents a novel methodology by combining Genetic Algorithm (GA) and Monte Carlo (MC) simulation approaches to simultaneously optimize cost allocation and system configuration in complex network. GA is used to generate configuration-cost pairs while MC is used to evaluate the reliability of the system for each pair. The application of the developed methodology is demonstrated for power grids as an example of critical complex networks. The results show that the proposed methodology can be readily used in practice.

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Hazard and Operability analysis (HAZOP) is a popular technique for hazard identification and risk ranking in hazardous facilities. Conventional HAZOP, however, has some drawbacks: (i) it considers a limited number of risk factors, i.e., only the frequency and the severity of hazards; (ii) it assumes equal weights for the risk factors, thus ranking low-probability high-consequence hazards equally important as high-probability low-consequence hazards; and (iii) it uses crisp and precise data which is rarely available or highly uncertain, especially in the case of complex oil and gas facilities.
The present study is an attempt to alleviate the foregoing drawbacks of conventional HAZOP via a Fuzzy Multi-Attribute HAZOP technique (FMA-HAZOP). To do this, Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used, both in a fuzzy environment, to determine the weight of risk factors and to prioritize the hazards. The application of the FMA-HAZOP on a gas wellhead facility shows that FMA-HAZOP presents a more transparent and more detailed information about the rank of hazards compared to conventional HAZOP.@en

Optimal selection of safety measures (SMs) is a challenging task for safety managers due to its importance, complexity, and incapability of traditional approaches in considering all the aspects of SMs optimal selection. Sophisticated mathematical models can be used to overcome the limitations of traditional approaches. However, setting the objective functions while considering their priorities as well as possible synergistic effects of the SMs on the hazards are still among the main concerns in the development and application of mathematical models.
The present study is aimed at developing a methodology to optimize the SMs selection while addressing the aforementioned challenges and considering both the budget and the risks. To do so, first the Pareto set of the solutions is obtained by NSGA-II technique - a multi-objective genetic algorithm technique - where a lexicographic model is used to select the optimal solution from the Pareto set based on the priority of the objective functions. A pessimistic strategy is used to account for the synergistic effects and the overlaps between the selected SMs.
Two mathematical models are developed to represent different policies in optimal SMs selection in a gas wellhead and surface facility. The results show a notable difference between the two policies, indicating the importance of setting proper objective functions in multi-objective optimization problems. The results also show that the methodology is able to effectively satisfy different safety management policies and constraints with no need for much extra information except the cost and impact of SMs on the hazards’ risk@en