Genetic Algorithms in the Framework of Dempster-Shafer Theory of Evidence for Maintenance Optimization Problems

作     者：Compare, Michele Zio, Enrico 

作者机构：Politecn Milan Dept Energy I-20133 Milan Italy Ecole Cent Paris & Supelec European Fdn New Energy Elect France Syst Sci & Energet Challenge Paris France 

出 版 物：《IEEE TRANSACTIONS ON RELIABILITY》 (IEEE Trans Reliab)

年 卷 期：2015年第64卷第2期

页      面：645-660页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：China NSFC 

主　　题：Evidence theory genetic algorithms pareto dominance maintenance optimization 

摘      要：The aim of this paper is to address the maintenance optimization problem when the maintenance models encode stochastic processes, which rely on parameters that are imprecisely known, and when these parameters are only determined through information elicited from experts. A genetic algorithms (GA)-based technique is proposed to deal with such uncertainty setting;this approach requires addressing three main issues: i) the representation of the uncertainty in the parameters and its propagation onto the fitness values;ii) the development of a ranking method to sort the obtained uncertain fitness values, in case of single-objective optimization;and iii) the definition of Pareto dominance, for multi-objective optimization problems. A known hybrid Monte Carlo-Dempster-Shafer Theory of Evidence method is used to address the first issue, whereas two novel approaches are developed for the second and third issues. For verification, a practical case study is considered concerning the optimization of maintenance for the nozzle system of a turbine in the Oil & Gas industry.