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Mathematical Problems in Engineering
Volume 2017, Article ID 3493687, 12 pages
https://doi.org/10.1155/2017/3493687
Research Article

Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2Shanghai Aircraft Customer Service Co., Ltd., Shanghai 200241, China

Correspondence should be addressed to Jing Cai; nc.ude.aaun@gnijiac

Received 2 September 2016; Accepted 12 February 2017; Published 20 March 2017

Academic Editor: Inmaculada T. Castro

Copyright © 2017 Jing Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.