A simulation based optimization approach for spare parts forecasting and selective maintenance

Equipment of the Army encounters various modes of exploitation depending on the scenario in which it is used. Typically, the missions are followed by intervals which can be used for maintenance. This is a suitable condition for employment of selective maintenance strategy. However, this maintenance interval is bound by the constraints of time, resources and desired reliability before the start of the next mission. This calls for optimization of maintenance activities that can be fitted into the maintenance break. There is also a requirement of having a forecasting technique for reducing the supply lead times. This paper lays out a methodology to use simulation for predicting failures in the army equipment. A Genetic Algorithm (GA) based approach is then used for optimizing the maintenance activities before the start of the maintenance break. The process of Simulation plus GA Optimization is automated using a program in MATLAB. The novelty of the work lies in modifying the process of Simulation and GA Optimization to suit the exact modus operandi employed by the Army in deploying equipment for peace, training exercise and war (mission with or without some maintenance break) separately. In addition to optimizing the maintenance activities, the methodology also helps in forecasting the requirement of spare parts both before and during the mission. (C) 2017 Elsevier Ltd. All rights reserved.