In this video, Yuting Sun describes a case study at a local small manufacturer of medical devices and applies a novel approach of production system modeling to overcome various practical challenges in collecting up- and downtime data of the operations. Specifically, the parametric model of the production system is identified based on system performance metrics derived from the parts flow data. With the model constructed, system bottleneck is analyzed and then, to enhance system throughput, potential improvement actions including operation speed-up, downtime reduction, and buffer expansion are explored. Finally, model sensitivity is analyzed by comparing the deviation of the model-predicted performance metrics to those produced by a reference nominal model.

In this video, Tianyu Zhu studies the buffer occupancy data in two-machine Bernoulli serial lines that are subject to noise and develops an effective algorithm to automatically detect and correct errors in these data. Numerical experiments show that the proposed method is capable of restoring data integrity and significantly improving system performance estimation accuracy using the corrected data.

In this video, Yuting Sun studies the problem of system performance metrics data-based parameter identification in synchronous two-machine exponential line model, develops two optimization algorithms: barrier method with BFGS quasi-Newton algorithm and cyclic coordinate descent method with proximal point update and verifies the efficacy and the accuracy of machine parameter estimation of the proposed algorithms via numerical experiments based on both theoretically calculated and simulation performance metrics data.

In this video, Yuting Sun discusses the efficacy of statistical learning methods in solving the problem of production system model parameter identification and demonstrates the good performance of three common used methods applied to parameter identification in Bernoulli serial line models through numerical experiments.

In this video, Jiachen Tu shows the application of feedforward neural network in estimating model parameter of exponential serial production lines, based on system’ s key performance metrics. Numerical experiments are carried out and results are presented to demonstrate the estimation accuracy of this new approach.

In this video, Tianyu Zhu analyses the method to standardize and automate the modeling process using standard manufacturing key performance indices in the framework of Bernoulli serial production line model. An algorithm is developed to identify the model parameters based on system throughput and work-in-process.