This thesis is centred on modelling and multi-objective optimization of Safety Instrumented Systems (SIS) in compliance with the standard IEC 61508. SIS are in charge of monitoring that the operating conditions of a plant remain under safe limits and free of hazards. Their performance is, therefore, critical for the integrity of people around the plant, the environment, assets and production. \ud \ud A large part of this work is devoted to modelling of SIS. Safety integrity and reliability measures, used as optimization objectives, are quantified by the Average Probability of Failure on Demand (PFDavg) and the Spurious Trip Rate (STR). The third objective is the Lifecycle Cost (LCC); ensuring system cost-effectiveness. The optimization strategies include design and testing policies. This encompasses optimization of design by redundancy and reliability allocation, use of diverse redundancy, inclusion of MooN voting systems and optimization of testing frequency and strategies. \ud \ud The project implements truly multi-objective optimization using Genetic Algorithms. A comprehensive analysis is presented and diverse applications to optimization of SIS are developed. Graphical techniques for presentation of results that aid the analysis are also presented.\ud \ud A practical approach is intended. The modelling and optimization algorithms include the level of modelling detail and meet the requirements of IEC 61508. The focus is on systems working in low-demand mode. It is largely based on the requirements of the process industry but applicable to a wide range of other process.\ud \ud Novel contributions include a model for quantification of time-dependent Probability of Failure on Demand; an approximation for STR; implementation of modelling by Fault Trees with flexibility for evaluation of multiple solutions; and the integration of system modelling with optimization by Genetic Algorithms. Thus, this work intends to widen the state-of-the-art in modelling of Probability of Failure on Demand, Spurious Trip Rate and solution of multi-optimization of design and testing of safety systems with Genetic Algorithms.\u
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