The increasing complexity of modern manufacturing systems poses significant challenges for production planning and control. While technological advancements have separately enhanced predictive maintenance and production scheduling, existing approaches still lack their simultaneous integration. As a result, inefficient resource utilization, increased downtime, and inaccurate decisions are presented. This research proposes a conceptual framework for joint adaptive and intelligent predictive maintenance and production scheduling. A literature review is conducted to identify current challenges and integration gaps, which serve as the foundation for the framework. The proposed solution is structured around three core modules: a simulation platform for performance evaluation, a meta-learning system for adaptive prognostics, and a reinforcement learning system for real-time production planning optimization. Supporting elements for ensuring ease of integration, data management and system interoperability are also introduced. The framework aims to enhance operational efficiency in dynamic manufacturing environments through artificial intelligence driven methods.
Caballero et al. (Thu,) studied this question.