Modeling for Non-exponential Production Systems Using Parts Flow Data: Model Parameter Estimation and Performance Analysis

TL;DR

This paper introduces a neural network-based surrogate modeling approach for estimating parameters of non-exponential production systems from parts flow data, enabling efficient system analysis and robustness verification.

Contribution

It extends performance metrics-based modeling to non-exponential systems using neural network surrogates and optimization, revealing parameter non-uniqueness and robustness insights.

Findings

Multiple parameter sets can produce similar system performance.

Neural network surrogates effectively estimate performance metrics.

Parameter estimations show linear relationships in reliability parameters.

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.