Several faculty members have recently assembled to form a System Informatics and Control (SIAC) group within the H. Milton Stewart School of Industrial and Systems Engineering. The aim of the SIAC group is to develop research and education programs that will provide a new scientific base for the design, analysis and control of complex manufacturing and service systems, anchored upon the effective and seamless integration of physical and analytical models with empirical data-driven methodologies.

This group brings together researchers who share a common interest in the development of quantitative models unified with data extraction and engineering knowledge integration capabilities and the employment of these models in the analysis, and control of complex manufacturing and service systems.

The key members of the SIAC group include:
* Jianjun (Jan) Shi, Carolyn J. Stewart Chair
* Nagi Gebraeel, Assistant Professor
* Jye-Chyi (JC) Lu, Professor
* Spiridon Reveliotis, Associate Professor
* Kwok-Leung Tsui, Professor
* Roshan Joseph Vengazhiyil, Associate Professor

Shi, Gabraeel, Lu, Reveliotis, Tsui, and Vengazhiyil bring a diverse set of experiences to the group with each providing a different background related to manufacturing and service systems, quality and reliability engineering, diagnostics and prognostics, industrial statistics and data mining, automation and control.

A new Ph.D. specialization on SIAC in ISyE has been approved by the Graduate Committee of Georgia Tech. The group has been actively developing new curricula with three core courses for SIAC Ph.D. specialization (e.g. ISyE 6810: System Monitoring and Prognostics, ISyE 7201: Production Systems Engineering, ISyE 7204: Informatics in Production and Service Systems), recruiting Ph.D. students (6 Ph.D. students have joined the new specialization at this time), and securing research grants.

Since July, 2009, they been awarded five National Science Foundation (NSF) grants that include:

GOALI: Causation-Based Quality Control - A new paradigm to achieve effective monitoring, diagnosis, and control for complex manufacturing systems: Focusing on "causation-based" quality control methodologies, this project will advance the state of the art in modeling and control of complex systems by contributing new concepts, criteria, and algorithms to the information-processing capabilities for quality improvement. The implementation of the resulting methodologies is expected to generate broad economic impacts.

Optimized Scheduling of Complex Resource Allocation Systems through Approximate Dynamic Programming: This project's focus is the development of a novel framework for managing the complex resource allocation that takes place in contemporary production and service systems. The results of this research will bring closer the existing developments in scheduling theory to the field practice.

Collaborative Research: Adaptive Maintenance Planning Based on Evolving Residual Life Distributions: The focus of this project is the development of broadly applicable analytical and statistical tools that determine adaptive maintenance policies for complex systems that deteriorate over time. This research can improve the way that firms translate vast quantities of condition monitoring data into maintenance decisions. Performing the right type of maintenance activity at the right time will reduce maintenance costs while improving safety.

Collaborative Research: Validation, Calibration, and Prediction of Computer Models with Functional Output: The proposed research focuses on Bayesian approach for calibration, validation, prediction, and experimental design of computer models with functional output. The major impact of the proposed research is to improve the effectiveness of computer model developers (model analysts) and users (scientists and engineering designers) in scientific understanding, as well as in design and manufacturing in various important scientific and engineering applications.

Robust optimization of nanoparticle synthesis in a supercritical CO2 process for energy
applications: Recognizing that systematic methods are needed to quantify uncertainty in nanomanufacturing processes, and subsequently to design processes that are robust to these uncertainties, the proposed work presents a comprehensive methodology for robust optimization of a batch process, using various sources of information integrated by a rigorous Bayesian method. The current disconnect between the fields of robust design in statistics and mechanistic modeling in engineering will be bridged by the proposed methodology. Focusing on the synthesis of metal nanoparticles, which are used in a wide range of applications from energy to medicine, this unique modeling approach for mean and variance of process variables is required to derive the recipe for a robust optimal process.

For more information about SIAC, visit http://www2.isye.gatech.edu/siac/.