International Journal of Industrial Engineering & Production Research
Iran University of Science & Technology
Mon, Jul 15, 2024
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Volume 35, Issue 3 (IJIEPR- In Progress 2024)
IJIEPR 2024, 35(3): 1-19 |
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cheikh K, Boudi E M, Mokhliss H, Rabi R. Development and Optimization of Maintenance Using the Monte Carlo Method. IJIEPR 2024; 35 (3) :1-19
URL: http://ijiepr.iust.ac.ir/article-1-1994-en.html
URL: http://ijiepr.iust.ac.ir/article-1-1994-en.html
1- 1. Department of Mechanical Engineering, Energetic team,Mechanical and Industrial Systems (EMISys), Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco. , khamiss_cheikh@um5.ac.ma
2- Department of Mechanical Engineering, Energetic team,Mechanical and Industrial Systems (EMISys), Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco.
3- Department of Physics (LPM-ERM), Faculty of Sciences and Techniques, Sultan Moulay Sliman University, B.P.523, 23000 Beni-Mellal, Morocco.
2- Department of Mechanical Engineering, Energetic team,Mechanical and Industrial Systems (EMISys), Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco.
3- Department of Physics (LPM-ERM), Faculty of Sciences and Techniques, Sultan Moulay Sliman University, B.P.523, 23000 Beni-Mellal, Morocco.
Abstract: (140 Views)
Maintenance plan efficacy traditionally prioritizes long-term predicted maintenance cost rates, emphasizing performance-centric approaches. However, such criteria often neglect the fluctuation in maintenance costs over renewal cycles, posing challenges from a risk management perspective. This study challenges conventional solutions by integrating both performance and robustness considerations to offer more suitable maintenance options.
The study evaluates two representative maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. It introduces novel metrics to assess these approaches, including long-term expected maintenance cost rate as a performance metric and variance of maintenance cost per renewal cycle as a robustness metric.
Mathematical models based on the homogeneous Gamma degradation process and probability theory are employed to quantify these strategies. Comparative analysis reveals that while higher-performing strategies may demonstrate cost efficiency over the long term, they also entail greater risk due to potential cost variability across renewal cycles.
The study underscores the necessity for a comprehensive evaluation that balances performance and resilience in maintenance decision-making. By leveraging the Monte Carlo Method, this research offers a critical appraisal of maintenance strategies, aiming to enhance decision-making frameworks with insights that integrate performance and robustness considerations.
The study evaluates two representative maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. It introduces novel metrics to assess these approaches, including long-term expected maintenance cost rate as a performance metric and variance of maintenance cost per renewal cycle as a robustness metric.
Mathematical models based on the homogeneous Gamma degradation process and probability theory are employed to quantify these strategies. Comparative analysis reveals that while higher-performing strategies may demonstrate cost efficiency over the long term, they also entail greater risk due to potential cost variability across renewal cycles.
The study underscores the necessity for a comprehensive evaluation that balances performance and resilience in maintenance decision-making. By leveraging the Monte Carlo Method, this research offers a critical appraisal of maintenance strategies, aiming to enhance decision-making frameworks with insights that integrate performance and robustness considerations.
Keywords: Maintenance Planning, Robustness, Block Replacement Strategy, Periodic Inspection And Replacement Strategy, Monte Carlo Method.
Type of Study: Research |
Subject:
Reliability and Maintenance
Received: 2024/03/1 | Accepted: 2024/06/26 | Published: 2024/09/18
Received: 2024/03/1 | Accepted: 2024/06/26 | Published: 2024/09/18
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