Combining simulation experiments and analytical models with area-based accuracy for performance evaluation of manufacturing systems

Abstract

Simulation is considered as one of the most practical tools to estimate manufacturing system performance, but it is slow in its execution. Analytical models are generally available to provide fast, but biased, estimates of the system performance. These two approaches are commonly used distinctly in a sequential approach, or one as alternative to the other, for assessing manufacturing system performance. This article proposes a method to combine simulation experiments with analytical results in a single performance evaluation model. The method is based on kernel regression and allows considering more than one analytical methods. A high-fidelity model is combined with low-fidelity models for manufacturing system performance evaluation. Multiple area-based low-fidelity models can be considered for the prediction. The numerical results show that the proposed method is able to identify the reliability of low-fidelity models in different areas and provide estimates with higher accuracy. Comparison with alternative approaches shows that the method is more accurate in a studied manufacturing application.

KEYWORDS:

• Performance evaluation
• simulation
• analytical modeling
• multi-fidelity regression
• manufacturing systems

Additional information

Notes on contributors

Ziwei Lin

Ziwei Lin is a jooint Ph.D. candidate in the Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University and Department of Mechanical Engineering, Politecnico di Milano. Her thesis focuses on performance evaluation and optimization of manufacturing systems based on multi-fidelity models.

Andrea Matta

Andrea Matta is professor of manufacturing at the Department of Mechanical Engineering at Politecnico di Milano, where he currently teaches integrated manufacturing systems and manufacturing. He is a guest professor at the School of Mechanical Engineering of Shanghai Jiao Tong University. His research area includes analysis and design of manufacturing and health care systems. He is Editor-in-Chief of the Flexible Services and Manufacturing Journal.

J. George Shanthikumar

J. George Shanthikumar is the Richard E. Dauch Chair of Manufacturing and Operations Management and Distinguished Professor of Management at Purdue University. His research interests are in integrated interdisciplinary decision making, model uncertainty and learning, production systems modeling and analysis, queueing theory, reliability, scheduling, semiconductor yield management, simulation stochastic processes, and sustainable supply chain management.