Parameter Identification for Multiple-Machine Bernoulli Lines using Statistical Learning Methods

Abstract

A manufacturing process is typically modeled as a stochastic process in production systems research. A challenge commonly met in practical applications is the identification of the parameters for such stochastic process models. The conventional approach focuses on collecting data from each individual work-station’s operation and relies on the modeler’s training and experience to convert the raw data into model parameters. A new modeling approach has recently been proposed that uses system performance metrics (e.g., throughput, work-in-process) as inputs to reversely calculate the model parameters. In this paper, we investigate the efficacy of statistical learning methods in solving the problem of production system model parameter identification. Specifically, three commonly used methods, multivariate regression, random forest, and artificial neural network, are applied to parameter identification in Bernoulli serial line models. Numerical experiments demonstrate the performance of these methods and show that the machine parameters can be identified with high accuracy.