Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models
Alexander Brodsky, Guodong Shao, Mohan Krishnamoorthy, Anantha Narayanan Narayanan, Daniel Menasc¿, Ronay Ak
In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by performing diagnostic tests on a composite process performance model.
, Shao, G.
, Krishnamoorthy, M.
, Narayanan, A.
, Menasc¿, D.
and Ak, R.
Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8094
(Accessed December 1, 2023)