Industrial Scheduling in the Digital Era: Challenges, State-of-the-Art Methods, and Deep Learning Perspectives

Industrial scheduling remains a pivotal discipline for efficiency, resilience, and competitiveness in manufacturing and service operations. The digital transformation, driven by paradigms such as Industry 4.0, has fundamentally reshaped the scheduling landscape, introducing pervasive connectivity, real-time data flows, and cyber-physical integration. This review synthesizes advances across three enduring challenges: (i) scalability and computational complexity in large-scale, high-dimensional scheduling environments, (ii) robustness and adaptability to uncertainty and disruptions, and (iii) integration with digitalization through IIoT, digital twins, cloud–edge architectures, and interoperable, secure infrastructures. We highlight the surge of AI-enhanced and deep learning–driven methods that are redefining state-of-the-art practice. Deep reinforcement learning (DRL) now underpins policy learning for dynamic dispatching and rescheduling; graph neural networks (GNNs) and attention models enable generalization across diverse shop configurations; and digital twin–in-the-loop frameworks provide safe training and rapid adaptation under real-world volatility. At the same time, neural architectures are increasingly embedded within decomposition, metaheuristics, and multi-agent systems—forming hybrid stacks that combine the guarantees of operations research with the adaptability of learning-based approaches. The industrial impact of these developments is evident across semiconductor fabrication, flexible job shops, supply-chain–intensive production, and distributed, autonomous networks. Yet critical challenges persist in interpretability, trust, data quality, and legacy integration. To advance, future research must prioritize explainable and certifiable neural schedulers, standardized datasets and benchmarks, seamless AI–IoT–DT integration, federated and privacy-preserving collaboration, and human-in-the-loop frameworks. Collectively, these directions chart a path toward scheduling systems that are not only more efficient and scalable, but also transparent, secure, and resilient in the digital, interconnected era.