DDPS | “A DDPS Engineering Approach for Supply Chain Management and Enterprise-Wide Optimization” 

DDPS Talk date: April 12, 2024
· Speaker: Burcu Beykal (University of Connecticut, beykal.engr.uconn.edu/biosketch/)
· Description: Current industrial processes require the coordination of many interconnected pieces that involve multi-dimensional, multi-purpose, and multi-product systems. Across the different layers of supply chain management, starting from the supply chain structure to production planning and scheduling, the optimal coordination of each element and their robust response to changing market conditions is essential for increasing the efficiency, resiliency, productivity, and profitability of any enterprise. Yet, the modeling and optimization of such interdependent systems are still burdensome and require a holistic approach to ensure feasible realizations of the individual activities of the supply chain. Bi-level programming is well-suited for the task, as scheduling problems (followers) provide constraints for the decision making in the planning problem (leader). However, there are many algorithmic challenges for this class of mathematical programs, especially when high numbers of integer variables are present in the scheduling problems. In this talk, I will demonstrate how such large-scale complex optimization problems can be solved without the full knowledge of the underlying mathematical models using data-driven modeling and global optimization theory.
· Bio: Dr. Burcu Beykal is an Assistant Professor in the Department of Chemical & Biomolecular Engineering and a resident faculty in the Center for Clean Energy Engineering at University of Connecticut. She holds a B.S. degree in Chemical & Biological Engineering from Koc University, an M.S. degree in Chemical Engineering from Carnegie Mellon University, and a Ph.D. degree in Chemical Engineering from Texas A&M University. Before joining UConn, Burcu was a Postdoctoral Research Associate at the Texas A&M Energy Institute. Her research focuses on data-centric process systems engineering and machine learning of energy-critical systems, spanning chemical, environmental, and biological domains. Among her awards are the American Chemical Society Petroleum Research Fund Doctoral New Investigator Award, the 2020 CAST Directors’ Award, and the Outstanding Graduate Student Award from Texas A&M. She was also selected as a Rising Star in Chemical Engineering by the Massachusetts Institute of Technology in 2019.
DDPS webinar: www.librom.net/ddps.html
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IM release number is: LLNL-VIDEO-863438