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Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

Prerequisites: IE 411, IE 511, and MATH 464 or equivalent. Description: A wide variety of decision-making problems in engineering, science, and economics involve uncertain parameters whose values are unknown to the decision maker when the decisions are made. The underlying uncertainty of these problems may arise from incomplete data, measurement errors, or the inherently stochastic nature of the respective problems. Ignoring this uncertainty can lead to inferior solutions that perform poorly in practice. The goal of this course is to introduce optimization models and methodologies that address uncertainty-affected decision problems. The course will introduce fundamental techniques from stochastic programming, robust optimization, and distributionally robust optimization. The theory will be motivated through concrete examples from production planning, supply chain management, project management, portfolio selection, and machine learning.