<![CDATA[This research discusses the maintenance problem of a small commercial aircraft with propeller engine, typed ATR-72. Based on the maintenance records, the aircraft has average 294 routine activities that have to be monitored and done based on determined threshold interval. This research focuses on developing a metaheuristic model to optimize the aircraft?s utility, called Crow Search Algorithm (CSA) to solve the Aircraft Maintenance Problem (AMP). The algorithm is developed and tested whether a younger metaheuristic method, CSA, is able to give better performance compared to the older methods, Particle Swarm Optimization (PSO) and other hybridized method PSO with Greedy Randomized Adaptive Search Optimization (PSO-GRASP). Several experiments are performed by using parameters: 1000 maximum iteration and 600 maximum computation time by using four dataset combinations. The results show that CSA can give better performance than PSO but worse than PSO-GRASP.]]>
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