Multi-fidelity modeling for analysis of serial production lines

Abstract

Analytical and simulation models are two common types of approaches used to estimate and predict the performance of complex production systems. Typically analytical models are fast to run but can have reduced accuracy. On the other hand simulation models can achieve high accuracy, but only at the cost of large simulation time and number of replications. Traditionally, the research has been focusing on the development of models able to achieve a satisfactory trade off between accuracy and computational effort. Nevertheless, such an approach implies the choice of a single model to approximate the system behavior. There is still lack of a generic model that can deliver high accuracy and low computational cost for production systems. In this paper, we attempt to address this issue and present a multi-fidelity modeling approach, utilizing both analytical models and simulation models at different levels of fidelity, to efficiently and effectively estimate the performance of asynchronous serial lines with exponential machines. Experimental results show that the multi-fidelity model provides better estimation of the production rate of the studied example Such a model has demonstrated potential in evaluating a large number of solutions with limited computational budget.