Level of Repair Analysis based on Genetic Algorithm with Tabu Search

Genetic algorithms and their hybrid schemes have shown a great efficacy in solving large scale combinatorial problems in which solutions are highly time-consuming. The level of repair analysis (LORA), mathematically formulised by an integer programming model (IP), is very difficult to optimize by means of traditional optimization techniques due to a large number of decision variables involved. In this paper, a hybridised Genetic Algorithm with Tabu Search is presented and its application to solve Level of repair analysis (LORA) problem is investigated. The LORA, considered as an important tool for strategic system maintenance decision making, seeks to determine the location in the repair network at which a failed component should be discarded or repaired. The proposed algorithm is developed in order to determine the best repair decision combination. The efficacy of the algorithm is investigated in the context of a case study. The maintenance costs of a structure of three-echelon repair and multi-indenture is optimised under the condition that repair decision should be taken for all system items. Typical results have shown that the algorithm can effectively handle a real industrial sized case study with adequate optimisation computational time.