Industrial engineers both create and use data analytics approaches in order to not just gain insight into data, but to also utilize data for making optimal decisions.
Faculty
Suchi Rajendran
Kangwon Seo
- Simulation-based Search for Optimal Experimental Design
- Battery Capacity Prediction by Functional Monitoring Data
Sharan Srinivas
- Discovering Airline-specific Intelligence from Online Reviews
Projects
Simulation-based Search for Optimal Experimental Design
We introduce a practical method to find an optimal design of experiments for ALTs by using simulation and empirical model building.
The design found by our proposed approach shows substantially improved statistical performance in terms of the standard error of estimates of 10th percentile of failure time.
Our approach provides useful insights about sensitivity of each decision variable to the objective function.
Battery Capacity Prediction by Functional Monitoring Data
Research Motivation
- Conventional approaches to battery prognostic mostly focused on exploring the capacity degradation data.
- Seldom tried using other performance measures such as temperature, voltage, and current monitoring data that could be useful to predict the capacity.
Methodology Framework
- Applying functional principal component analysis (fPCA), each curve of the monitoring variable is expressed as functional PC scores.
- Adopt a shrinkage method (Lasso regression) with fPC scores.
Discovering Airline-specific Intelligence from Online Reviews
Project objectives
- Analyze current state-of-the-art text analytics approach for understanding online customer reviews
- Identify key dimensions of service quality for an airline from online passenger reviews
- Develop machine-learning-based approach to automatically classify a review sentence into a service quality dimension and appropriate sentiment
- Extract business and competitive intelligence from online reviews