Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios

Abstract

This paper presents a methodology to support the selection of optimal portfolios of preventive safety measures for time-dependent accident scenarios. This methodology captures the dynamics of accident scenarios through Dynamic Bayesian Networks which represent the temporal evolution of component failures that can lead to system failure. An optimization model is presented to determine all Pareto optimal portfolios for which the residual risk of the system at different time stages is minimized, subject to budget and technical constraints on the set of feasible portfolios. The resulting portfolios are then analyzed to support the optimal selection of preventive safety measures. We also develop a computationally efficient algorithm for solving the multi-objective optimization model. The method is illustrated by revisiting the accident scenario of a vapor cloud ignition which occurred at Universal Form Clamp in Bellwood (Illinois, U.S.) on 14 June 2006. Results are presented for different cost levels of implementing the preventive safety measures, which provides additional management insights.

Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system

“…[2]. Gates represents logic structures of the Bayesian model in our research article [1]. The failure probabilities in Table 6 have been obtained by reducing the initial failure probability of the components, based on a specific reduction rate for each preventive safety measure.…”

“…The failure probabilities in Table 6 have been obtained by reducing the initial failure probability of the components, based on a specific reduction rate for each preventive safety measure. These values illustrate the viability of the Bayesian model [1], but do not represent any actual system. The occurrence probabilities of the outcomes of the accident scenarios have been computed by GeNIe Modeler [4] through the Dynamic Bayesian Network presented in our research article [1].…”

“…These values illustrate the viability of the Bayesian model [1], but do not represent any actual system. The occurrence probabilities of the outcomes of the accident scenarios have been computed by GeNIe Modeler [4] through the Dynamic Bayesian Network presented in our research article [1]. Finally, the severity of the outcomes has been quantified through the trade-off weighing approach SWING [5].…”

“…Specifically, we revisit the earlier analyses of the accident scenarios by Khakzad et al. [2] to illustrate the methodology presented in our research article [1]. One of such accident scenarios occurred on 14 June 2006 at Universal Form Clamp in Bellwood (Illinois, U.S.) through a vapor cloud ignition [3].…”

Modelling a Safety Management System Using System Dynamics at the Bhopal Incident

“…INCIDENT: the incident is the manifestation of an occurred error in the decision-making process or in the execution of the maintenance/inspection activities. The number of incidents as well as the arising consequences become the parameters by which the safety management must measure to implement all strategies to try to avoid them [17]. • Safety management system: The International Civil Aviation Organization (ICAO) defines the safety management system as an organized approach to managing safety, which includes the necessary organizational structure, the definition of the politics and procedures [18].…”