Constrained optimization of test intervals using a steady-state genetic algorithm

There is a growing interest from both the regulatory authorities and the nuclear industry to stimulate the use of Probabilistic Risk Analysis (PRA) for risk-informed applications at Nuclear Power Plants (NPPs). Nowadays, special attention is being paid on analyzing plant-specific changes to Test Int...

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Vydáno v:Reliability engineering & system safety Ročník 67; číslo 3; s. 215 - 232
Hlavní autoři: Martorell, S., Carlos, S., Sánchez, A., Serradell, V.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2000
Témata:
Constrained optimization
Cost and burden
Genetic algorithms
Probabilistic risk assessment
Resource-effectiveness
Risk-consistency check
Risk-informed decision-making
Safety-related systems availability
Technical specifications
Test intervals
Test intervals
Safety-related systems availability
Technical specifications
Resource-effectiveness
Probabilistic risk assessment
Risk-informed decision-making
Cost and burden
Risk-consistency check
Constrained optimization
Genetic algorithms
ISSN:0951-8320, 1879-0836
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Shrnutí:There is a growing interest from both the regulatory authorities and the nuclear industry to stimulate the use of Probabilistic Risk Analysis (PRA) for risk-informed applications at Nuclear Power Plants (NPPs). Nowadays, special attention is being paid on analyzing plant-specific changes to Test Intervals (TIs) within the Technical Specifications (TSs) of NPPs and it seems to be a consensus on the need of making these requirements more risk-effective and less costly. Resource versus risk-control effectiveness principles formally enters in optimization problems. This paper presents an approach for using the PRA models in conducting the constrained optimization of TIs based on a steady-state genetic algorithm (SSGA) where the cost or the burden is to be minimized while the risk or performance is constrained to be at a given level, or vice versa. The paper encompasses first with the problem formulation, where the objective function and constraints that apply in the constrained optimization of TIs based on risk and cost models at system level are derived. Next, the foundation of the optimizer is given, which is derived by customizing a SSGA in order to allow optimizing TIs under constraints. Also, a case study is performed using this approach, which shows the benefits of adopting both PRA models and genetic algorithms, in particular for the constrained optimization of TIs, although it is also expected a great benefit of using this approach to solve other engineering optimization problems. However, care must be taken in using genetic algorithms in constrained optimization problems as it is concluded in this paper.
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ISSN:0951-8320
1879-0836
DOI:10.1016/S0951-8320(99)00074-5