A data-enabled mathematical model in describing a production line with variable cycle time machines, in which machine and system constraints are ensured and the sensor data such as random machine downtimes are enabled as inputs.

Lab Contributors: Chen, Jing

Paper Website: https://www.sciencedirect.com/science/article/pii/S2351978921000664

Development of a robust and explainable data-enabled mathematical model that can evaluate the system performance of an assembly line with limited real-time sensor data.\ Establishment of a dynamic bottleneck detection methodology based on the developed model and physical understanding of the highly stochastic system.\ Further quantification of the production loss attributed to each bottleneck, providing a powerful tool for industrial engineers to quickly identify the bottleneck station that leads to the largest production loss and take action to eliminate the problem.

Lab Contributors: Chen

Paper Website: https://www.sciencedirect.com/science/article/pii/S2213846323001463

Based on the data-enabled model, this project propose an analytical analysis method to efficiently identify real-time permanent production loss (PPL) attributed to each machine, and evaluate their opportunity windows for such systems.

Lab Contributors: Chen, Jing

Paper Website: https://ieeexplore.ieee.org/document/9466325