Optimal opportunistic maintenance policy using genetic algorithms, 1: formulation

Abstract

Describes the development of two genetic algorithm (GA) programs for cost optimization of opportunity‐based maintenance policies. The combinatorial optimization problem is formulated and it is shown that genetic algorithms are particularly suited to this type of problem. The theoretical basis and operations of a standard genetic algorithm (SGA) are presented with an iterative procedure necessary for implementation of the SGA to least‐cost part replacement. However, an SGA used in an iterative manner may limit the global search capability of the evolutionary computing technique and may lead to suboptimal solutions. To avoid this problem, an improved GA which considers more than two groups simultaneously is devised. This model is based on the permutation representation and genetic sequencing operators originally developed for the travelling salesman problem. The same example used with the SGA confirmed that the improved GA can bring additional savings.

Development of opportunistic maintenance policy towards optimal maintenance system (OPTOMS) model

“…When one component fails, there is a high possibility one or more components are affected and need to be maintained. Thus, the opportunity may be taken during the shutdown or CM activities to carry out PM on other maintenancesignificant components that have the potential to fail in the near future (Savic et al, 1995).…”

Short communication optimal replacement scheduling in multi-state series-parallel systems

“…GAs have been proven to be effective optimization tools for a great number of applications. Successful applications of GAs for reliability engineering problems [10][11][12][18][19][20][21][22][23][24][25][26][27] and, in particular, for maintenance policy optimization [10][11][12]26,27] are reported.…”

“…Minimal repair, which enables the system element to continue its work but does not affect the hazard rate of the element, may be much less expensive [1,2]. Maintenance policies of compromising between preventive replacements and minimal repairs aim at achieving an optimal solution for problems with different criteria and have been addressed in a number of works [2][3][4][5][6][7][8][9][10][11][12]. All these works considered binary-state system reliability.…”

Monte Carlo simulation approach for a modified FMECA in a power plant

“…The maintenance selection problem has just been treated in the literature adopting several techniques: genetic algorithms [2,3], an analytic hierarchy process [4], multi-attribute utility theory [5]. However, considering the importance and the complexity of this problem, further efforts concerning the development of effective methods that are able to incorporate numerous evaluation criteria and to help maintenance staff in intangible factors evaluations are useful, recommended and important in practice.…”