Reliability-Centered Maintenance and Cost Optimization Strategies for Heavy-Duty Commercial Vehicle Fleets: A Comprehensive Review
Year: 2026
Type: article
Abstract: Heavy commercial vehicle fleets play a crucial role in transportation, logistics, mining, and construction sectors, where vehicle reliability and maintenance efficiency directly influence operational performance and profitability. The increasing complexity of fleet operations, coupled with rising maintenance expenditures and unexpected vehicle failures, has highlighted the need for advanced maintenance management strategies. This review article systematically investigates recent developments in reliability-centered maintenance (RCM), reliability assessment techniques, and maintenance cost optimization methods applied to heavy commercial vehicle fleets. Various reliability evaluation approaches, including Weibull analysis, Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Reliability Block Diagrams (RBD), Markov models, and Remaining Useful Life (RUL) prediction methods, are critically reviewed. Furthermore, emerging technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Digital Twin technology, and Industry 4.0 frameworks are analyzed for their potential to improve fleet reliability and reduce maintenance costs. The review also examines mathematical and metaheuristic optimization techniques, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), and NSGA-II, for maintenance scheduling and cost minimization. Finally, existing research gaps, future trends, and opportunities for intelligent, sustainable, and data-driven fleet maintenance systems are identified. The findings provide valuable insights for researchers and fleet managers seeking to enhance vehicle availability, operational efficiency, and long-term reliability.
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204127275
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