Research
IEOR researchers investigate the latest mathematical tools, approaches, and methodologies to make new theoretical discoveries and innovations that touch nearly every industry, making them more efficient and profitable in areas such as supply chain, logistics, manufacturing, data science, energy systems, robotics, and management.
Selected Publications
Using telephone call rates and nurse-to-patient ratios as measures of resilient performance under high patient flow conditions
Miller, A., Aswani, A., Zhou, M. et al. Using telephone call rates and nurse-to-patient ratios as measures of resilient performance under high patient flow conditions. Cogn Tech Work 21, 225–236 (2019). https://doi.org/10.1007/s10111-018-0498-7
A Mixed-Integer Linear Programming Method for Optimal Orificing in Breed-and-Burn Cores
Chris Keckler, Alper Atamturk, Massimiliano Fratoni and Ehud Greenspan. A Mixed-Integer Linear Programming Method for Optimal Orificing in Breed-and-Burn Cores. Transactions of the American Nuclear Society, Vol. 118, 887-890, 2018. https://atamturk.ieor.berkeley.edu/pubs/orificing.pdf.
Strong Formulations for Quadratic Optimization with M-matrices and Indicator Variables
Alper Atamturk and Andres Gomez. Strong Formulations for Quadratic Optimization with M-matrices and Indicator Variables. Mathematical Programming 170, 141-176, 2018. https://link.springer.com/article/10.1007%2Fs10107-018-1301-5.
Sparse and Smooth Signal Estimation: Convexification of L0 Formulations
Alper Atamturk, Andres Gomez and Shaoning Han. “Sparse and Smooth Signal Estimation: Convexification of L0 Formulations”. Journal of Machine Learning Research.
A two-step gradient estimation approach for setting supply chain operating parameters
KAMINSKY AND S. LIU. 2018.A Two-Step Gradient Estimation Approach to for Setting Supply Chain Operating Parameters. Computers & Operations Research 92, pp. 98-110