A Novel Simulation-Taguchi-DEA approach for Optimum Maintenance Scheduling

In this paper, a new approach for maintenance policy and planning problem is developed. First, maintenance activities are simulated by incorporating learning effects. Production and maintenance functions are estimated using historical data. Then, simulation is carried out for different scenarios which are combinations of periodic maintenance and different policies. Several outputs including machines and operators’ availability, reliability, efficiency and queue length are computed. Since the problem is multi-criteria, data envelopment analysis (DEA) method is used to select the preferred policy. In order to show the applicability of the proposed approach the data for a series production line is used and different scenarios with different policies are investigated. Since many scenarios are needed to be simulated, Taguchi orthogonal array design is used to reduce the number of scenarios. The proposed approach of this study would help managers to identify the preferred strategy considering and investigating various parameters and policies. This is the first study that introduces an integrated multi-criteria approach for optimum maintenance policy and planning.