Maintenance Optimization Using Fuzzy Logic Controlled Genetic Algorithm
Abstract
In the field of reliability management, using preventive maintenance is one of the major operations. Optimizing the maintenance policy is required so as to provide a low-cost and efficient function of the system. This paper deals with the problem of maintenance optimization in a multi-states series-parallel system. The objective is to optimize for each system component the maintenance policy minimizing a cost function of the system under the constraint of required availability and for a specified period. Since inspections in the beginning of the life of a component, when the component is very reliable are not efficient, the maintenance policy should identify the dates of first inspection of each system component. We propose using an evolutionary algorithm called fuzzy logic controlled genetic algorithm (FLC-GA) to solve this optimization problem. Two controllers are used to adaptively adjust the crossover and the mutation probabilities based on the fitness function and on the degree of population diversity. Simulation results are presented for the proposed method which is compared to a genetic algorithm with fixed crossover and mutation probabilities. The experimental results show the advantages and the efficiency of FLC-GA.