Evaluating the performance and robustness of PIR and QIR maintenance strategies using Monte Carlo method

Cheikh Khamiss (), Boudi El Mostapha (), Rabi Rabi (), Mokhliss Hamza () and Ennaji Hamadi ()
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Cheikh Khamiss: Department of Mechanical Engineering, Energetic Team, Mechanical and Industrial Systems (EMISys), Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco
Boudi El Mostapha: Department of Mechanical Engineering, Energetic Team, Mechanical and Industrial Systems (EMISys), Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco
Rabi Rabi: Department of Physics (LPM-ERM), Faculty of Sciences and Techniques, Sultan Moulay Sliman University, B. P. 523, 23000 Beni-Mellal, Morocco
Mokhliss Hamza: Department of Physics, Laboratory of Electronics, Instrumentation and Energetics, Faculty of Sciences, Chouaib Doukkali University, El Jadida, Morocco
Ennaji Hamadi: LMA, Faculty of Sciences and Techniques, Sultan Moulay Sliman University, B. P. 523, 23000 Beni-Mellal, Morocco

Monte Carlo Methods and Applications, 2025, vol. 31, issue 1, 43-58

Abstract: In the literature, maintenance plan efficacy is often assessed based on the long-term predicted maintenance cost rate, indicating a performance-centric approach. However, this criteria does not account for the fluctuation in maintenance costs over renewal cycles, and typical solutions may not be adequate from a risk management standpoint, a robustness viewpoint. This study tries to rethink standard solutions considering both performance and robustness, and thus, offer more suitable maintenance options.Specifically, using the long-term expected maintenance cost rate as the performance metric and the variance of maintenance cost per renewal cycle as the robustness metric, the study examines two representatives of time-based and condition-based maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. Mathematical cost models are created based on the homogeneous Gamma degradation process and probability theory.Comparative study of both maintenance techniques demonstrates that the higher-performing approach carries a larger amount of risk. Consequently, a full examination of both performance and resilience is required in selecting a more dependent maintenance option. These maintenance solutions, together with the employment of the Monte Carlo Method, are contrasted against each other using a unique criteria that analyzes the degree of performance and robustness of each adaptation in maintenance decision-making.

Keywords: Optimization; robustness; condition-based maintenance; periodic inspection; quantile-based inspection; renewal process; Monte Carlo method (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/mcma-2025-2001

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