Welcome to the Industrial Engineering and Operations Research Department at the University of California at Berkeley. In IEOR, we invent, analyze and teach tools and approaches for design, analysis, risk management, and decision-making in complex real-world systems like supply chains, energy systems, healthcare systems, and financial systems.
The Department of Industrial Engineering and Operations Research combines two closely related professions concerned with the efficient operation of complex systems.
Economic analysis for engineering decision making: Capital flows, effect of time and interest rate. Different methods of evaluating of alternatives. Minimum-cost life and replacement analysis. Depreciation and taxes. Uncertainty: preference under risk; decision analysis. Capital sources and their effects. Economic studies.
News & Events
The Department of Industrial Engineering & Operations Research is excited to announce Dr. Zeyu Zheng will be joining the IEOR department as an assistant professor starting in fall 2018.
“I feel excited to bridge operations research and data sciences, and to push data analytics methodologies to new heights. I can't think of a better environment than IEOR for this form of research.”
Zeyu recently earned a Ph.D. in management science and engineering and a Ph.D. minor in statistics from Stanford University. Previously, he earned a masters degree in economics from Stanford, and a B.S. in mathematics from Peking University. His research interests include simulation, data analytics, stochastic modeling, statistical learning and inference, and financial technologies.
Welcome back golden bears! The annual Fall 2018 picnic is on September 20, 2018. IEOR undergraduate and graduate students are invited to come and enjoy free Top Dog with faculty and staff. The event will take place at the Upper Doe Terrance. See you there!
Research Expertise and Interest
process systems engineering, control theory, energy systems, biological systems
The goal of Professor Mesbah's research is to develop systems analysis techniques and application-relevant control theory for complex chemical and biological systems. The theoretical developments are applied toward societal problems, including energy systems and biomedical applications. His group's research efforts provide a balance between theory, computation, and real-world applications that span from nano- and micro-length scale systems to traditional large-scale (bio)chemical engineering systems.
Have a big idea for the future of U.S. STEM research?
The National Science Foundation (NSF) just announced the launch of the NSF 2026 Idea Machine, a prize competition to help set the U.S. agenda for fundamental research in science, technology, engineering, and mathematics (STEM) and STEM education. Participants can earn cash prizes and receive public recognition by suggesting the pressing research questions that need to be answered in the coming decade, the next set of “Big Ideas” for future investment by NSF. It's an opportunity for researchers, the public and other interested stakeholders to contribute to NSF's mission to support basic research and enable new discoveries that drive the U.S. economy, enhance national security and advance knowledge to sustain the country's global leadership in science and engineering.
Entries will be accepted through October 26, 2018. For more information, including entry instructions, eligibility, rules, and judging criteria, please visit the NSF 2026 Idea Machine website.
George Dantzig is considered a founding father of industrial engineering and operations research, but one of his most incredible achievements took place while he was still a doctoral student at Berkeley. Arriving late to a statistics class, Dantzig scrawled down two problems written on the blackboard, thinking that they were a homework assignment. He solved the problems and handed them in, only to learn weeks later that these were not homework, but two famously unsolved statistics problems. The story became legendary, inspiring a scene in the movie Good Will Hunting.
But that breakthrough was just one of Dantzig’s numerous accomplishments. In 1960, Dantzig was an early faculty hire in what was then the Department of Industrial Engineering, where he founded and directed the Operations Research Center. He became the first person to formulate linear programming models and investigate their mathematical properties. He also developed the simplex algorithm, which was foundational to the field of mathematical optimization and operations research. Together, his work on linear programming and the simplex method has touched nearly every industry. A member of the National Academy of Engineering, National Academy of Sciences and the American Association of Arts and Sciences, Dantzig was honored with the National Medal of Science in 1975.
Originally posted at Berkeley Engineering
Abstract: High tech products such as smartphones or wearables are multi-generational in the sense that every year, a new version of the product is introduced, with some features that are shared with the previous year’s model as well as added functionality. This setting poses unique problems for manufacturers in making operational decisions regarding production and warranty servicing of these products. For each generation, at some point, a decision must be made to stop producing that version of the product. However, this decision often comes before the items themselves are out of warranty. How should companies manage future warranty claims? Professor Candace Yano and PhD student Erik Bertelli are focused on optimal production and warranty decisions in settings where warranty fulfillment may involve provision of new replacements of the same model, repair using spare parts inventory, and rebates for upgrading to a newer generation of product. This research will enable the manufacturer not only to minimize the expected costs for satisfying warranty claims, but also to reduce electronic waste from unnecessary replacement items and spare parts.
Bio: Erik Bertelli is a third year PhD student in the Industrial Engineering and Operations Research department at UC Berkeley. His research focuses on inventory control for multi-generational high-tech products withuncertain warranty obligations.
Matt Olfat - Fair Optimization: Deep Notions of Fairness in Machine Learning
Abstract: Classification algorithms such as Support Vector Machines (SVM) output an array of probabilities or score functions, which are then thresholded to generate binary predictions. The prevalence of these machine learning methods in commercial use has raised concerns of their ability to codify and amplify biases existent in data. Previous techniques to address this problem mainly rely on pre- or post-processing steps; they are often blind to score functions underlying predictions, making their fairness guarantees non-robust to changes in thresholding. Furthermore, pre- and post-processing is inherently greedy and thus may be prone to excessively penalizing accuracy. In response, we propose the framework of Fair Optimization to enforce robust fairness at training time. The goal of this approach is to capture independence through constraints tractable in an optimization framework, effectively controlling the discriminatory ability of a learner within the learning stage. We show that our method is able to find fair classifiers that retain accuracy for a number of real datasets, and present generalizations of our method to the setting of unsupervised learning as well.
Bio: Matt Olfat is a PhD candidate in IEOR at UC Berkeley. He received his B.S. in Systems Engineering and Mathematics from the University of Virginia in 2014, and his M.S. in Industrial Engineering and Operations Research from UC Berkeley in 2016. His research interests include fairness in machine learning, applications of machine learning in public policy, and decompositions of high-dimensional datasets.