# Real-Time Maintenance Optimization with Industrial Internet of Things
## 2 Literature Review  
During the literature review, we primarily relied on sources available in Scopus, a comprehensive database of scholarly articles. This allowed us to gather relevant research from a wide range of academic disciplines, ensuring that our review is based on high-quality, peer-reviewed publications. [PERSON] et al. (2025) develop an integrated model combining quality control and maintenance by accounting for quality-dependent failures [2]. However, their approach relies on offline optimization and lacks the real-time adaptability enabled by IIoT technologies. [PERSON] et al. (2025) propose a joint strategy for production, inspection, and maintenance under finite time constraints, emphasizing the impact of time limits on cost efficiency [3]. Still, their model operates within fixed planning horizons and does not incorporate live system feedback like the IIoT-based approach. [PERSON] et al. (2025) apply deep reinforcement learning to optimize maintenance in multi-component systems by integrating quality and production planning into decision-making [4]. Despite its advanced algorithm, the model is trained in simulated settings and lacks real-time system integration. [PERSON] and [PERSON] (2024) review AI-driven methods in smart manufacturing, identifying trends and gaps in production, maintenance, and quality optimization [5]. While insightful, their work is descriptive and retrospective, offering less practical value than the real-time, data-driven solution proposed in this article. [PERSON] et al. (2024) introduce a joint optimization model for job scheduling, condition-based maintenance, and spare parts ordering in degrading systems [6]. [PERSON] et al. (2024) present a machine learning framework that combines decision trees with reinforcement learning for production, maintenance, and quality control optimization in flexible systems [7]. Their model, however, does not utilize real-time sensor data from IIoT systems, which limits its ability to dynamically adjust maintenance and optimization strategies based on real-time state information. [PERSON] et al. (2024) propose a system-level predictive maintenance model for no-wait production environments with machine-robot collaboration [8]. Their method focuses on economic dependencies and hybrid fault modes but does not integrate real-time operational data, which would enable continuous monitoring and adaptive decision-making like IIoT-based solutions. [PERSON] et al. (2024) develop a condition-based maintenance optimization strategy for multi-equipment batch production systems under stochastic demand, using Monte Carlo simulations to determine maintenance timings [9]. Although their approach effectively addresses variability in demand, it lacks the capability for real-time monitoring and adaptation, which IIoT systems provide for more responsive maintenance planning. [PERSON] (2024) presents an integrated model for inventory, maintenance, and quality control in production systems with multiple operating states and quality disruptions [10]. While the model uses adaptive control schemes and periodic sampling, it does not leverage continuous IIoT data, limiting its potential to optimize operations in real-time to fixed decision points. [PERSON] et al. (2023) propose a maintenance strategy for production systems subject to shock environments, incorporating buffer inventory and production defects into the optimization process [11]. However, their method relies on hybrid algorithms like Monte Carlo and Genetic Algorithms (GA) without using real-time state information, which would improve responsiveness and efficiency in real-time maintenance management. [PERSON] et al. (2023) present a mathematical optimization model for production scheduling that integrates preventive maintenance in uncertain production environments [12]. The model helps determine an optimal production schedule while considering machine usage, maintenance activities, and uncertainties. It saves costs by reducing unnecessary machine use.