Scheduling in Industry 4.0: A Digital Twin-based approach for scheduling and smart Material-Handling Considerations

Smart manufacturing constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twin (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. In this article, it is argued that a pre-DT optimal approach employing queuing aspects of the machine buffers can play a crucial role in optimally determining the baseline schedules for the shop as well as a few related system-design aspects vis-à-vis the size of the utilized fleet of smart Automated Guided Vehicles (sAGVs) and the employed buffer capacities. sAGVs are autonomous vehicles used for material transportation between machines, reducing manual handling and improving efficiency. Initial dispatching rules for the sAGVs are also determined at that stage. Such initially produced schedules and sAGV dispatching rules are constantly revisited, though, later in the development lifecycle of the manufacturing system at the DT level, according to the undertaking disruptions on the shop floor. At that DT stage, other operational aspects pertaining to the material handling system, namely, aisle directionality, mobile modular buffers, and input/output points of the work centers, are adjusted. The employed two-stage planning framework, integrating both Pre-DT and full-scale DT planning, aims to optimize aspects of the system from the design phase to its real-time operations, employing a novel methodology leveraging mathematical programming, queuing models, and deep learning. A key finding of this study is that dynamically adjusting aisle directionality, rerouting AGVs through alternative paths, and deploying modular mobile buffers while optimizing job scheduling significantly reduce transportation time, minimize delays, and enhance real-time adaptability. The proposed framework effectively mitigates disruptions, achieving 100% elimination of machine failure impact, a 33% reduction in aisle congestion delays, and a 37% decrease in buffer overflow delays, demonstrating notable improvements in system performance and resilience.