IE 598

Spring 2024 Part of Term 1

Part of Term 1
Jan 16-May 1

Special Topics

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Credit: 1 TO 4 hours.

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.

1 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

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IE 598 class schedule data for spring 2024
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	70691	Lecture-Discussion	JG	3:30PM -4:50PM	TR	2055 Sidney Lu Mech Engr Bldg	Garg, J Zhang, X	Part of Term: 1 Date Range: 01/16/24-05/01/24 Credit: 4 hours Section Title: Game Theory and Fair Division Section Info: Prerequisites: IE 310 or equivalent; basic knowledge of optimization, probability, and linear algebra; mathematical maturity. The course will explore various topics at the intersection of economics and computation whose solutions have been deployed to solve a wide range of real-life settings such as assigning medical residents to hospitals, allocating students to schools, assigning seats in courses, kidney exchange, refugee allocation, assigning public housing, airport traffic management, and so on. The course will cover the topics in foundations of game theory and fair division such as Nash equilibrium, bargaining, mechanism design, fair and efficient allocation of goods/chores, and their computation. Restriction(s): Restricted to Graduate - Urbana-Champaign.
	47494	Lecture	OU	2:00PM -3:20PM	TR	147 Loomis Laboratory	Hanasusanto, G Park, H	Part of Term: 1 Date Range: 01/16/24-05/01/24 Credit: 4 hours Section Title: Optimization Under Uncertainty Section Info: 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. Restriction(s): Restricted to Graduate - Urbana-Champaign.