Selecting optimum maintenance activity plans by a unique simulation-multivariate approach

Abstract

This study presents an integrated computer simulation-multivariate approach for selection of optimum maintenance activity plans. First, maintenance activities are modelled by computer simulation. Production and maintenance functions are estimated using historical data. Then, simulation is carried out for different scenarios, which are combinations of periodic maintenance and number of maintenance crew. Several outputs including machines and operators’ availability, reliability, efficiency and queue length are computed. Four multivariate methods, namely data envelopment analysis (DEA), artificial neural network (ANN), principal component analysis (PCA) and numerical taxonomy (NT) are used to select the optimum policy. Finally, statistical methods are used to select the most reliable method for selecting the optimum scenarios.

Keywords:

• maintenance activity plan
• computer simulation
• data envelopment analysis
• artificial neural network
• principal component analysis
• numerical taxonomy

Acknowledgements

The authors are grateful for the valuable comments and suggestions by the respected reviewers, which enhanced the strength and significance of this work. The authors are also grateful for the support provided by the College of Engineering, University of Tehran, Iran.

Additional information

Funding

This study was supported by a grant from the Iran National Science Foundation [grant no. 93010029]. The authors are grateful for the financial support provided by the Iran National Science Foundation. This study was also supported by a grant from the University of Tehran [grant no. 8106013/1/19].