Modeling for Non-exponential Production Systems Using Parts Flow Data: Model Parameter Estimation and Performance Analysis
Yuting Sun, Liang Zhang
TL;DR
The paper addresses parameter identification for non-exponential production lines using parts flow data, proposing neural-network surrogates to rapidly compute performance metrics and a multi-start PSO to recover machine parameters from observed metrics. It demonstrates that multiple non-unique parameter sets can yield practically indistinguishable system performance, with a robust negative linear relation between overall downtime and average CV across valid solutions. The approach enables efficient model validation and sensitivity analysis, offering a pathway to high-fidelity, data-driven design and control of smart manufacturing systems. The work also explores the transformation between non-exponential and exponential models and discusses practical implications for model selection and applicability in varying buffer and downtime scenarios.
Abstract
Mathematical modeling of production systems is the foundation of all model-based approaches for production system analysis, design, improvement, and control. To construct such a model for the stochastic process of the production system more efficiently, a new modeling approach has been proposed that reversely identifies the model parameters using system performance metrics (e.g., production rate, work-in-process, etc.) derived from the parts flow data. This paper extends this performance metrics-based modeling approach to non-exponential serial production lines. Since no analytical expressions of performance metrics are available for non-exponential systems, we use neural network surrogate models to calculate those performance metrics as functions in terms of the system parameters. Then, based on the surrogate models and given performance metrics, the machine parameters are estimated by solving a constrained optimization problem that minimizes the mean square error of the performance metrics resulting from the estimated parameters compared to the true ones. With the designed multi-start particle swarm optimization algorithm, we find that multiple non-unique combinations of machine parameters can lead to practically the same system performance metrics and a linear relationship of the reliability parameters from these obtained estimations is observed. Besides, model sensitivity analysis is implemented to verify the robustness of the different combinations of machine parameters even under the potential improvement scenarios.