Advanced optimization and comparative assessment of time- and condition-based maintenance strategies for assets subjected to stochastic degradation: A Gamma process approach incorporating performance-robustness metrics

This research develops and rigorously evaluates a comprehensive analytical framework for the optimization and comparative assessment of time-based and condition-based maintenance strategies applied to assets experiencing stochastic degradation. Asset deterioration is modeled using a homogeneous Gamma process, accurately representing monotonic degradation processes subject to inherent variability. The study systematically investigates three maintenance policies : Block Replacement (BR), Periodic Inspection and Replacement (PIR), and Quantile-based Inspection and Replacement (QIR). To quantitatively balance economic efficiency and cost predictability, a novel performance-robustness objective function ϕ is introduced, integrating the expected maintenance cost rate and its variability into a unified metric. Extensive Monte Carlo simulations demonstrate that QIR, through the dynamic adjustment of inspection intervals based on conditional reliability thresholds, consistently yields the lowest expected maintenance costs and superior control of cost variability across a wide spectrum of economic scenarios. In contrast, BR is shown to incur significantly higher expected costs and pronounced variability, while PIR offers moderate improvements by incorporating fixed-interval inspections. Sensitivity analyses further reveal that optimal maintenance parameters must adapt to fluctuations in downtime cost rates, underscoring the necessity of responsive, condition-based strategies. The proposed methodological framework and objective function provide maintenance practitioners with a robust decision-making tool for selecting strategies that minimize total cost of ownership while ensuring financial stability, thereby supporting the adoption of adaptive, data-driven maintenance practices in modern industrial asset management.

Authors

Institutions

Publication Details

Journal
Results in Materials
Published
2026-02-13
DOI
https://doi.org/10.1016/j.rinma.2026.100911
Primary Topic
Reliability and Maintenance Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advanced optimization and comparative assessment of time- and condition-based maintenance strategies for assets subjected to stochastic degradation: A Gamma process approach incorporating performance-robustness metrics

Rabi Rabi, Hamza Mokhliss, Elmostapha Boudi, Khamiss Cheikh
Results in Materials
Reliability and Maintenance Optimization
article

Advanced optimization and comparative assessment of time- and condition-based maintenance strategies for assets subjected to stochastic degradation: A Gamma process approach incorporating performance-robustness metrics

Rabi Rabi, Hamza Mokhliss, Elmostapha Boudi, Khamiss Cheikh
article en

Abstract

This research develops and rigorously evaluates a comprehensive analytical framework for the optimization and comparative assessment of time-based and condition-based maintenance strategies applied to assets experiencing stochastic degradation. Asset deterioration is modeled using a homogeneous Gamma process, accurately representing monotonic degradation processes subject to inherent variability. The study systematically investigates three maintenance policies : Block Replacement (BR), Periodic Inspection and Replacement (PIR), and Quantile-based Inspection and Replacement (QIR). To quantitatively balance economic efficiency and cost predictability, a novel performance-robustness objective function ϕ is introduced, integrating the expected maintenance cost rate and its variability into a unified metric. Extensive Monte Carlo simulations demonstrate that QIR, through the dynamic adjustment of inspection intervals based on conditional reliability thresholds, consistently yields the lowest expected maintenance costs and superior control of cost variability across a wide spectrum of economic scenarios. In contrast, BR is shown to incur significantly higher expected costs and pronounced variability, while PIR offers moderate improvements by incorporating fixed-interval inspections. Sensitivity analyses further reveal that optimal maintenance parameters must adapt to fluctuations in downtime cost rates, underscoring the necessity of responsive, condition-based strategies. The proposed methodological framework and objective function provide maintenance practitioners with a robust decision-making tool for selecting strategies that minimize total cost of ownership while ensuring financial stability, thereby supporting the adoption of adaptive, data-driven maintenance practices in modern industrial asset management.

Results in MaterialsVol. 29
Mohammed V University (MA), Ecole Mohammadia d'Ingénieurs (MA), Université Sultan Moulay Slimane (MA), Chouaib Doukkali University (MA)
Openalex Percentile: Top 66%
Reliability and Maintenance Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.