Document Type : Original Research Article
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University, Tehran, Iran
Department of Management and Accounting, College of Farabi, University of Tehran, Iran
Industrial Engineering Department, Faculty of Industry, Management and Accounting, Shahabdanesh University, Qom, Iran
Associate Professor, College of Farabi, University of Tehran, Iran
Industries' increasing progress and complexity has made maintenance and repair tasks very challenging, complex, and time-consuming. Maintenance is one of the important sectors in several industries, and improvement in this sector can have excellent results. This paper develops a new maintenance prediction model based on Bayesian networks (BN) capabilities. The models include several variables that experts determine and their influence on each other's-called conditional probability tables-which are learned from historical data. The model is implemented in an automobile repair department case study to show its performance. The model is evaluated through a sensitivity analysis, and the results show the proficiency of the proposal mode.