Decision Analytics Lab aims to develop intelligent decision-making methods for multifaceted systems originated from healthcare, manufacturing, energy and other industries. Our research uses an extensive array of problem-solving methods: Operations Research, Machine Learning, and Artificial Intelligent techniques. These areas offer unique research potential in formulating new theories, discovering challenging application areas, and exploring their intersections.
The expanding areas of our interdisciplinary work includes:
- The use of big data analytics to assess healthcare policies and recommend intervention for improved healthcare service and patient outcomes.
- The development of resilient strategies to combat the opioid addiction epidemic including risk assessment tools and incentive allocation model to deter the flow of excess Opioid to black markets and secondary users.
- Designing robust supply chain and resilient humanitarian logistic networks under the upshot of disruptions.
- Energy smart production plan for integrated manufacturing and remanufacturing systems operating under uncertainties.
- Development of pragmatic decision-making framework to handle inherent uncertainty and complexity of multi-criteria decision-making process.
- Designing machine learning assisted decision-making framework for wind energy power plants.
We assess the trade-offs in several parameters including cost, service quality, systems reliability, and resiliency to help decision-makers best mirror their true preferences while deriving high impact societal, business, and engineering decisions.