IE 598

Spring 2020 All Classes

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Credit: 0 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.

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 2020
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
66173	Lecture	ET	6:00PM -9:00PM	R	ARR Illini Center	Lariviere, D	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Electronic Trading Section Info: Prerequisites: IE 522 and IE 523. The purpose of this course is to investigate the exact nature of order matching and routing at the compute-packet level in most exchanges. Not knowing the nature the interfaces has led to many "good fit" predictive models. However, they are often, predicting the past! Analyses need to adjust for speed and time-stamps. The course will address these issues. However, it should be stressed that the course does not purport nor intend to examine nor propose "trading strategies." Restriction(s): Restricted to MS: Financial Engineering.
70691	Lecture-Discussion	JG	3:30PM -4:50PM	TR	1214 Siebel Center for Comp Sci	Garg, J	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Games, Markets, Math Prog Section Info: Prerequisites: IE 310 or equivalent; basic knowledge of optimization, probability, and linear algebra; mathematical maturity. This course will introduce students to the theory of games and markets and their strong connections to mathematical programming techniques. It will include solution concepts in game theory such as Nash equilibrium and correlated equilibrium, their computation; zero-sum games and minimax theorem; extensive form games; repeated games; competitive equilibrium in markets; utility maximization; strategic analysis; among others. It will be shown that many problems in these areas can be formulated as network flow, linear programming (LP), convex programming (CP), and complementarity (LCP, NCP) problems. Restriction(s): Restricted to Graduate - Urbana-Champaign.
47494	Lecture-Discussion	OU	3:30PM -4:50PM	TR	204 Transportation Building	He, N	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Optimization Under Uncertainty Section Info: Prerequisites: IE 410 or equivalent stochastic processes course; ECE 490, IE 510, IE 521, or equivalent optimization course. Description: Uncertainty penetrates in every corner of data science and decision science, from data generation, model selection, system dynamics, algorithm design, all the way to prediction and decision making. This course will offer a broad overview of the modeling, theories, algorithms, and applications for the vibrant field of optimization and learning under uncertainty. Topics include stochastic optimization, robust linear/conic programs, two-stage stochastic programming , chance constraint programming, risk-averse optimization, data-driven distributionally robust optimization, multi-stage stochastic programming, and Markov decision problems. We will cover a wide range of solution methods including stochastic approximation, Monte Carlo sampling methods, variance reduction techniques, decomposition methods, convex relaxation, dynamic programming, reinforcement learning algorithms, and etc. We will also discuss their wide applications in machine learning, financial engineering, operations management, power systems, and control. Restriction(s): Restricted to Graduate - Urbana-Champaign.
62526	Lecture-Discussion	XC	5:00PM -7:50PM	W	204 Transportation Building	Chen, X	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Pricing and Revenue Management Section Info: Prerequisites: IE 410 and IE 411. We will explore the state-of-the-art in pricing optimization and revenue management research. Topics that will be covered include • Quantity-based revenue management • Demand estimation and forecasting • Dynamic pricing • Assortment optimization • Learning Restriction(s): Restricted to Graduate - Urbana-Champaign.