Abstract
It has been proved to be of great significance to determine the carrying spare parts reasonably for promoting the probability of the system successfully completing a mission. Taking into account the effects of the in-situ repair capacity and random common cause failures occurred in the mission, a novel carrying spare parts optimization model is developed. The proposed model is to maximize the probability of the system successfully completing the next mission considering storage space constraints. Additionally, a solution algorithm integrating the famous Monte Carlo simulation and marginal algorithm is presented to solve the proposed model. Finally, a real example is given to verify the validity of the model, as well as the solution algorithm, and a detailed discussion is carry out to demonstrate the effects of the storage space on carrying spare part quantity and the associated probability of the system successfully completing the mission. The results show that: (1) the proposed model can be used for carrying spare part optimization considering in-situ maintenance capacity and random common cause failure; (3) the reasonable spare part storage space and carrying spare part quantity contribute to a great probability of the system successfully completing a mission.
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