Smart factories manage digital twins (DTs) to evaluate the performance of various what-if production scenarios. This article presents a DT consistency-checking approach to maintain DT in high fidelity by checking whether each sensed timed event from the physical manufacturing plant is under its corresponding DT-based estimations in runtime. The approach targets DTs developed using time colored Petri net (TCPN). To build the candidates of the next observable event with observable time margins, we considered the stochastic property of the plant, frequent external actuation caused by a new order, machine maintenance, etc., as well as intermediate unobservable state transitions reaching the sensible events. Based on the considerations, we propose an iterative method to build the virtual estimates for streaming physical events using efficiently evolved state-class graphs (SCGs). We also propose a TCPN partitioning method to accelerate the SCG-evolution and make DT maintenance easier by supporting the isolation of inconsistent subnets being diagnosed. We applied the approach to a USB flash-drive factory to prove the concept and evaluated the performance under various situations to show speedups of the SCG evolution, that is the crucial overhead of the estimation.

In smart manufacturing, engineers typically analyze unexpected real-time problems using digitally cloned discrete-event (DE) models for wafer fabrication. To achieve a faster response to problems, it is essential to increase the speed of DE simulations because making optimal decisions for addressing the issues requires repeated simulations. This paper presents a hierarchical aggregation/disaggregation (A/D) method that substitutes complex event-driven operations with two-layered abstracted models-single-group mean-delay models (SMDMs) and multi-group MDMs (MMDMs)-to gain simulation speedup. The SMDM dynamically abstracts a DE machine group's behaviors into observed mean-delay constants when the group converges into a steady state. The MMDM fast-forwards the input lots by bypassing the chained processing steps in multiple steady-state groups until it schedules the lots for delivery to subsequent unsteady groups after corresponding multi-step mean delays. The key component, the abstraction-level converter (ALC), has the roles of MMDM allocation, deallocation, extension, splitting, and controls the flow of each group's input lot by deciding the destination DE model, SMDM, and MMDMs. To maximize the reuse of previously computed multi-step delays for the dynamically changing MMDMs, we propose an efficient method to manage the delays using two-level caches. Each steady-state group's ALC performs statistical testing to detect the lot-arrival change to reactivate the DE model. However, fast-forwarding (FF) results in incorrect test results of the bypassed group's ALCs due to the missed observations of the bypassed lots. Thus, we propose a method for test-sample reinitialization that considers the bypassing. Moreover, since a bypassed group's unexpected divergence can change the multi-step delays of previously scheduled events, a method for examination of FF history is designed to trace the highly influenced events. This proposed method has been applied in various case studies, and it has achieved speedups of up to about 5.9 times, with 2.5 to 8.3% degradation in accuracy.

The recent development of new technologies within the Industry 4.0 revolution drives the increased digitization of manufacturing plants. To effectively utilize the digital twins, it is essential to guarantee a correct alignment between the physical system and the associated simulation model along the whole system life cycle. Data assimilation is frequently used to incorporate observation data into a running model to produce improved estimates of state variables of interest. However, it assumes a closed system and cannot handle structural changes in the system, e.g., machine breakdown. Instead of combining the observation data into an existing model, we aim to automatically generate the model concurrently with the data assimilation procedure. This can reduce the time and cost of building the model. In addition, it can generate a more accurate model when sudden operational changes are not reflected at the higher planning levels. Component-based model generation approach is used with the application of data and process mining techniques to generate a complete process model from the data. A new data assimilation method is proposed to iteratively generate new models based on the arrival of further data. Each model is simulated to obtain the system performance, which will be compared to the real system performance to select the best-estimated model. Identical twin experiments of a wafer-fab simulation are conducted under different scenarios to evaluate the feasibility of the proposed approach.

Speeding up the simulation of discrete-event wafer-fabrication models is essential for fast decision-making to handle unexpected events in smart semiconductor manufacturing because decisionparameter optimization requires repeated simulation execution based on the current manufacturing situation. In this paper, we present a runtime abstraction-level conversion approach for discrete-event fab models to gain simulation speedup. During the simulation, if the fab’s machine group model reaches a steady state, then the proposed method attempts to substitute this group model with a mean-delay model (MDM) as a high abstraction level model. TheMDMabstracts detailed event-driven operations of subcomponents in the group into an average delay based on the queuing modeling, which can guarantee acceptable accuracy in predicting the performance of steady-state queuing systems. To detect the steadiness, the proposed abstraction-level converter (ALC) observes the queuing parameters of low-level groups to identify the statistical convergence of each group’s work-in-progress (WIP) level. When a group’s WIP level is converged, the output-to-input couplings between the models are revised to change a wafer-lot process flow from the low-level group to a MDM. When the ALC detects lot-arrival changes or any wafer processing status change (e.g., a machinedown), the high-level model is switched back to its corresponding low-level group model. During highto- low level conversion, the ALC generates dummy wafer-lot events to re-initialize the machine states. The proposed method was applied to various case studies of wafer-fab systems and achieved simulation speedups up to about 4 times with 0.6 to 8.3% accuracy degradations

In cloud computing, multiple workflow scheduling is important to optimize resource allocation and utilization for concurrent executions of diverse workflows across different applications. While previous research has focused on clustering-based resource allocation to reduce communication overheads by grouping tasks, it often overlooks the significance of task execution ordering, limiting overall performance optimization. To address the limitation, we propose two genetic-based approaches, considering task and cluster-level characteristics, to introduce novel ordering techniques for multi-workflow scheduling under cluster-based resource allocation. By comparing two granularity ordering methods, we offer valuable insights for efficient task management in multi-workflow environments. Our experiments demonstrate that the proposed approaches, especially the task granularity-based ordering method, outperform existing primary clustering methods, particularly for scenarios involving a large number of workflows or highly parallel workflows.