OPTIMIZATION OF MACHINE PREVENTIVE MAINTENANCE SCHEDULING USING STEADY STATE GENETIC ALGORITHM
Maintenance management is one of the important factors to support the success of industrial activities. In order for an industry to have a high level of profit. Good maintenance management is needed to minimize costs lost due to the engine failure. Preventive maintenance activities are one of the company's efforts to be able to maintain the life span and engine performance. In conducting preventive maintenance activities, the company wants to maximize machine reliability with minimum costs. The existing maintenance activities implemented by the company are to doing the maintenance every 2 months, but with the implementation of this maintenance policy it still has many obstacles in its implementation. Therefore optimization is needed to overcome this problem, one of the methods proposed to do preventive maintenance scheduling is the steady state genetic algorithm optimization method. On completion, 3 types of fitness functions are used, Fitness function 1 is a fitness function by giving weights to total costs and reliability functions with conditions w1 + w2 = 1. Fitness function 2 is a fitness function that is used by having a given budget limit. While Fitness function 3 is a fitness function that is used to provide required reliability or reliability that the company wants to achieve. The input from the steady state genetic algorithm has 3 components, the time to failure distribution parameter, the cost and budget, and the iteration input from the genetic algorithm. Based on data that has a 2 parameter Weibull distribution with scale parameter lambda = 0.00184 and shape parameter beta = 1.38194. Found 3 preventive maintenance scheduling proposals for 24 months period. The first result using fitness function 1 produced a total cost of 28.66 million rupiahs with a reliability value of 91.78%. The second proposal using fitness function 2 produced a total cost of 29.75 million rupiahs with a reliability value of 92.47%. The third uses using fitness function 3 resulting in a total cost of 30.79 million rupiahs with a reliability value of 92.52%.
Keywords: Preventive maintenance, Optimization, Reliability, Total cost, Steady State Genetic Algorithm.
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