Twin Cities campus

This is archival data. This system was retired as of August 21, 2023 and the information on this page has not been updated since then. For current information, visit catalogs.umn.edu.

 
Twin Cities Campus

Industrial and Systems Engineering Ph.D.

Industrial and Systems Engineering
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Industrial and Systems Engineering Graduate Program, University of Minnesota, 100 Union Street SE, Minneapolis, MN 55455 (612-624-1582; fax: 612-624-0944)
Email: isye@umn.edu
Website: http://cse.umn.edu/isye/
  • Program Type: Doctorate
  • Requirements for this program are current for Fall 2021
  • Length of program in credits: 68
  • This program does not require summer semesters for timely completion.
  • Degree: Doctor of Philosophy
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The Industrial and Systems Engineering (ISyE) PhD program offers coursework and research in industrial and systems engineering, operations research, and human factors. Special emphasis is on methodologies for design, planning, and management of service and manufacturing systems. Examples of research applications include logistics, transportation, healthcare delivery systems, revenue management, and supply chain management.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
A baccalaureate degree in engineering or a closely related field is required.
Special Application Requirements:
All application materials should be submitted electronically through the online application system. The application deadlines are December 15 for fall semester and October 15 for spring semester.
Applicants must submit their test score(s) from the following:
  • GRE
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 79
    • Internet Based - Writing Score: 21
    • Internet Based - Reading Score: 19
  • IELTS
    • Total Score: 6.5
Key to test abbreviations (GRE, TOEFL, IELTS).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
32 credits are required in the major.
12 credits are required outside the major.
24 thesis credits are required.
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.00 is required for students to remain in good standing.
Courses offered on both the A-F and S/N grading basis must be taken A-F. In order to fulfill the University's graduate education policy regarding research ethics training, students are required to take an online research ethics training course through the CITI program, or a qualifying equivalent.
Required Courses (16 credits)
Take the following courses:
IE 8521 - Optimization (4.0 cr)
IE 8532 - Stochastic Processes and Queuing Systems (4.0 cr)
Select 8 credits from the following in consultation with the advisor:
IE 5511 - Human Factors and Work Analysis (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production and Inventory Systems (4.0 cr)
Seminars (2 credits)
Take 2 seminar credits from the following in consultation with the advisor. Other seminars may be applied to this requirement with advisor approval.
IE 8773 - Graduate Seminar (1.0 cr)
IE 8774 - Graduate Seminar (1.0 cr)
Outside Coursework (12 credits)
Select 12 credits from the following in consultation with the advisor. Other courses may be chosen with advisor and director of graduate studies approval.
APEC 8001 - Applied Microeconomic Analysis of Consumer Choice and Consumer Demand (2.0 cr)
APEC 8002 - Applied Microeconomic Analysis of Production and Choice Under Uncertainty (2.0 cr)
CSCI 5211 - Data Communications and Computer Networks (3.0 cr)
CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 8980 - Special Advanced Topics in Computer Science (1.0-3.0 cr)
ECON 8101 - Microeconomic Theory (2.0 cr)
ECON 8102 - Microeconomic Theory (2.0 cr)
ECON 8117 - Noncooperative Game Theory (2.0 cr)
ECON 8118 - Noncooperative Game Theory (2.0 cr)
ECON 8119 - Cooperative Game Theory (2.0 cr)
MATH 5485 - Introduction to Numerical Methods I (4.0 cr)
MATH 5486 - Introduction To Numerical Methods II (4.0 cr)
MATH 5615H - Honors: Introduction to Analysis I (4.0 cr)
MATH 5616H - Honors: Introduction to Analysis II (4.0 cr)
MATH 8601 - Real Analysis (3.0 cr)
MATH 8602 - Real Analysis (3.0 cr)
MATH 8651 - Theory of Probability Including Measure Theory (3.0 cr)
MATH 8652 - Theory of Probability Including Measure Theory (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
STAT 5303 - Designing Experiments (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
STAT 8101 - Theory of Statistics 1 (3.0 cr)
STAT 8102 - Theory of Statistics 2 (3.0 cr)
STAT 8501 - Introduction to Stochastic Processes with Applications (3.0 cr)
Electives
Select credits as needed from the following, in consultation with the advisor, to meet the 44 course credits required for the degree. Other courses may be selected with advisor and director of graduate studies approval.
IE 5080 - Topics in Industrial Engineering (1.0-4.0 cr)
IE 5511 - Human Factors and Work Analysis (4.0 cr)
IE 5513 - Engineering Safety (4.0 cr)
IE 5522 - Quality Engineering and Reliability (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production and Inventory Systems (4.0 cr)
IE 5561 - Analytics and Data-Driven Decision Making (4.0 cr)
IE 8531 - Discrete Optimization (4.0 cr)
IE 8533 - Advanced Stochastic Processes and Queuing Systems (4.0 cr)
IE 8534 - Advanced Topics in Operations Research (1.0-4.0 cr)
IE 8535 - Introduction to Network Science (4.0 cr)
IE 8536 - Advanced Topics in Engineering Management (4.0 cr)
IE 8538 - Advanced Topics in Information Systems (4.0 cr)
IE 8541 - Decision Support Systems (4.0 cr)
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems (4.0 cr)
IE 8794 - Industrial Engineering Research (1.0-6.0 cr)
Thesis Credits (24 credits)
Take 24 doctoral thesis credits after passing preliminary oral exam.
IE 8888 - Thesis Credit: Doctoral (1.0-24.0 cr)
Program Sub-plans
A sub-plan is not required for this program.
Students may not complete the program with more than one sub-plan.
Industrial Engineering
  
More program views..
View college catalog(s):
· College of Science and Engineering

View future requirement(s):
· Summer 2023
· Fall 2022

View PDF Version:
Search.
Search Programs

Search University Catalogs
Related links.

College of Science and Engineering

Graduate Admissions

Graduate School Fellowships

Graduate Assistantships

Colleges and Schools

One Stop
for tuition, course registration, financial aid, academic calendars, and more
 
IE 8521 - Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems. prereq: Familiarity with linear algebra and calculus.
IE 8532 - Stochastic Processes and Queuing Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to stochastic modeling and processes. Random variables, discrete and continuous Markov chains, renewal processes, queuing systems, Brownian motion, and elements of reliability and stochastic simulation. Applications to design, planning, and control of manufacturing and production systems. prereq: 4521 or equiv
IE 5511 - Human Factors and Work Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: HumF 5211/IE 5511/ME 5211
Grading Basis: A-F or Aud
Typically offered: Every Fall
Human factors engineering (ergonomics), methods engineering, and work measurement. Human-machine interface: displays, controls, instrument layout, and supervisory control. Anthropometry, work physiology and biomechanics. Work environmental factors: noise, illumination, toxicology. Methods engineering, including operations analysis, motion study, and time standards. prereq: Upper div CSE or grad student
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
IE 5551 - Production and Inventory Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Inventory control, supply chain management, demand forecasting, capacity planning, aggregate production and material requirement planning, operations scheduling, and shop floor control. Quantitative models used to support decisions. Implications of emerging information technologies and of electronic commerce for supply chain management and factory operation. prereq: CNR or upper div or grad student
IE 8773 - Graduate Seminar
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Recent developments.
IE 8774 - Graduate Seminar
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Recent developments. prereq: 8773
APEC 8001 - Applied Microeconomic Analysis of Consumer Choice and Consumer Demand
Credits: 2.0 [max 2.0]
Course Equivalencies: ApEc 8001/Econ 8001/Econ 8101
Grading Basis: A-F or Aud
Typically offered: Every Fall
The course provides a rigorous mathematical treatment of cost-benefit analysis in terms of the theory of how prices, income, preferences, and other factors affect consumer choices and the demand for goods and services. The optimization theories and economic models are developed with and without uncertainty. Part of four-course, year-long sequence (APEC 8001-2-3-4) prereq: [[5151 or ECON 3101 or ECON 5151 or intermediate microeconomic theory], [[MATH 2243, MATH 2263] or equiv]] or instr consent
APEC 8002 - Applied Microeconomic Analysis of Production and Choice Under Uncertainty
Credits: 2.0 [max 2.0]
Course Equivalencies: ApEc 8002/Econ 8002/Econ 8102
Grading Basis: A-F or Aud
Typically offered: Every Fall
The course provides a rigorous mathematical treatment of cost-benefit analysis in terms of the theory of how prices, technology, and other important factors affect producer decisions, the supply of goods and services, and the demand for productive resources. The optimization theories and economic models are developed with and without uncertainty. The course also explores the theory of price determination in competitive, monopoly, and monopsony markets. Part of four-course, year-long sequence (APEC 8001-2-3-4) prereq: [[8001 or ECON 8001 or ECON 8101], [[MATH 2243, MATH 2263] or equiv]] or instr consent
CSCI 5211 - Data Communications and Computer Networks
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4211/CSci 5211/INET 4002
Typically offered: Every Fall
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW. prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5421 - Advanced Algorithms and Data Structures
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 8980 - Special Advanced Topics in Computer Science
Credits: 1.0 -3.0 [max 27.0]
Typically offered: Every Fall & Spring
Lectures and informal discussions. prereq: instr consent
ECON 8101 - Microeconomic Theory
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Decision problems faced by the household and firm; theories of choice under conditions of certainty and uncertainty. Partial equilibrium analysis of competition and monopoly. General equilibrium analysis. Welfare economics: economic efficiency of alternative market structures, social welfare functions. Dynamics: stability of markets, capital theory. Seven-week course. prereq: 5151 or equiv, Math 2243 or equiv, concurrent registration is required (or allowed) in Math 5615 or concurrent registration in Math 8601, grad econ major or instr consent
ECON 8102 - Microeconomic Theory
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Decision problems faced by the household and firm; theories of choice under conditions of certainty and uncertainty. Partial equilibrium analysis of competition and monopoly. General equilibrium analysis. Welfare economics: economic efficiency of alternative market structures, social welfare functions. Dynamics: stability of markets, capital theory. Seven-week course. prereq: 8101, concurrent registration is required (or allowed) in Math 5615 or concurrent registration is required (or allowed) in Math 8601, grad econ major or instr consent
ECON 8117 - Noncooperative Game Theory
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Solution concepts for noncooperative games in normal form, including Nash and perfect equilibrium and stable sets of equilibria. Extensive form games of perfect and incomplete information, sequential equilibrium, and consequences of stability for extensive form. Applications including bargaining and auctions. Seven-week course. prereq: Math 5616 or equiv or instr consent
ECON 8118 - Noncooperative Game Theory
Credits: 2.0 [max 2.0]
Typically offered: Every Fall & Spring
Solution concepts for noncooperative games in normal form, including Nash and perfect equilibrium and stable sets of equilibria. Extensive form games of perfect and incomplete information, sequential equilibrium, and consequences of stability for extensive form. Applications including bargaining and auctions. Seven-week course. prereq: 8117
ECON 8119 - Cooperative Game Theory
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
Basics of cooperative game theory, emphasizing concepts used in economics. Games with and without transferable utility; the core, the value, and other solution concepts. Recent results, including potentials, reduced games, consistency, and noncooperative implementation of cooperative solution concepts. Seven-week course. prereq: 8104, Math 5616 or equiv or instr consent
MATH 5485 - Introduction to Numerical Methods I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Solution of nonlinear equations in one variable. Interpolation, polynomial approximation. Methods for solving linear systems, eigenvalue problems, systems of nonlinear equations. prereq: [2243 or 2373 or 2573], familiarity with some programming language
MATH 5486 - Introduction To Numerical Methods II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Numerical integration/differentiation. Numerical solution of initial-value problems, boundary value problems for ordinary differential equations, partial differential equations. prereq: 5485
MATH 5615H - Honors: Introduction to Analysis I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574
MATH 5616H - Honors: Introduction to Analysis II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615
MATH 8601 - Real Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Set theory/fundamentals. Axiom of choice, measures, measure spaces, Borel/Lebesgue measure, integration, fundamental convergence theorems, Riesz representation. prereq: 5616 or instr consent
MATH 8602 - Real Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Radon-Nikodym, Fubini theorems. C(X). Lp spaces (introduction to metric, Banach, Hilbert spaces). Stone-Weierstrass theorem. Basic Fourier analysis. Theory of differentiation. prereq: 8601 or instr consent
MATH 8651 - Theory of Probability Including Measure Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Probability spaces. Distributions/expectations of random variables. Basic theorems of Lebesque theory. Stochastic independence, sums of independent random variables, random walks, filtrations. Probability, moment generating functions, characteristic functions. Laws of large numbers. prereq: 5616 or instr consent
MATH 8652 - Theory of Probability Including Measure Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Conditional distributions and expectations, convergence of sequences of distributions on real line and on Polish spaces, central limit theorem and related limit theorems, Brownian motion, martingales and introduction to other stochastic sequences. prereq: 8651 or instr consent
PUBH 8442 - Bayesian Decision Theory and Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
STAT 5303 - Designing Experiments
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Analysis of variance. Multiple comparisons. Variance-stabilizing transformations. Contrasts. Construction/analysis of complete/incomplete block designs. Fractional factorial designs. Confounding split plots. Response surface design. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent
STAT 8101 - Theory of Statistics 1
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Review of linear algebra. Introduction to probability theory. Random variables, their transformations/expectations. Standard distributions, including multivariate Normal distribution. Probability inequalities. Convergence concepts, including laws of large numbers, Central Limit Theorem. delta method. Sampling distributions. prereq: Statistics grad major or instr consent
STAT 8102 - Theory of Statistics 2
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference. Sufficiency. Likelihood-based methods. Point estimation. Confidence intervals. Neyman Pearson hypothesis testing theory. Introduction to theory of linear models. prereq: 8101, Statistics graduate major or instr consent
STAT 8501 - Introduction to Stochastic Processes with Applications
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Markov chains in discrete and continuous time, renewal processes, Poisson process, Brownian motion, and other stochastic models encountered in applications. prereq: 5101 or 8101
IE 5080 - Topics in Industrial Engineering
Credits: 1.0 -4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Topics vary each semester.
IE 5511 - Human Factors and Work Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: HumF 5211/IE 5511/ME 5211
Grading Basis: A-F or Aud
Typically offered: Every Fall
Human factors engineering (ergonomics), methods engineering, and work measurement. Human-machine interface: displays, controls, instrument layout, and supervisory control. Anthropometry, work physiology and biomechanics. Work environmental factors: noise, illumination, toxicology. Methods engineering, including operations analysis, motion study, and time standards. prereq: Upper div CSE or grad student
IE 5513 - Engineering Safety
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Occupational, health, and product safety. Standards, laws, and regulations. Hazards and their engineering control, including general principles, tools and machines, mechanics and structures, electrical safety, materials handling, fire safety, and chemicals. Human behavior and safety, procedures and training, warnings and instructions. prereq: Upper div CSE or grad student
IE 5522 - Quality Engineering and Reliability
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 3522/IE 5522
Typically offered: Periodic Fall & Spring
Quality engineering/management, economics of quality, statistical process control design of experiments, reliability, maintainability, availability. prereq: [4521 or equiv], [upper div or grad student or CNR]
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
IE 5551 - Production and Inventory Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Inventory control, supply chain management, demand forecasting, capacity planning, aggregate production and material requirement planning, operations scheduling, and shop floor control. Quantitative models used to support decisions. Implications of emerging information technologies and of electronic commerce for supply chain management and factory operation. prereq: CNR or upper div or grad student
IE 5561 - Analytics and Data-Driven Decision Making
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language. Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
IE 8531 - Discrete Optimization
Credits: 4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Topics in integer programming and combinatorial optimization. Formulation of models, branch-and-bound. Cutting plane and branch-and-cut algorithms. Polyhedral combinatorics. Heuristic approaches. Introduction to computational complexity.
IE 8533 - Advanced Stochastic Processes and Queuing Systems
Credits: 4.0 [max 4.0]
Typically offered: Periodic Spring
Renewal and generative processes, Markov and semi-Markov processes, martingales, queuing theory, queuing networks, computational methods, fluid models, Brownian motion. prereq: 8532 or instr consent
IE 8534 - Advanced Topics in Operations Research
Credits: 1.0 -4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Special topics determined by instructor. Examples include Markov decision processes, stochastic programming, integer/combinatorial optimization, and queueing networks.
IE 8535 - Introduction to Network Science
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Topics include deterministic and random networks, network flows, matching, game theory, distributed decision making in networks, cooperation in networks, cascades in networks, wisdom of crowds, applications in voting, prediction markets, consumer behavior modeling, revenue management, inventory control and finance. This course is offered to graduate students. Undergraduate students must get permission from the instructor for registering. Prerequisites include probability and optimization (5531 and 8532) but students who have taken similar courses or have the mathematical background can register by instructor permission.
IE 8536 - Advanced Topics in Engineering Management
Credits: 4.0 [max 8.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Areas such as financial engineering, revenue management, management of health systems, service operations, management of technology, and public policy.
IE 8538 - Advanced Topics in Information Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Decision support methods. Case studies of specific systems. Methods for testing usability/performance. Trust/over-reliance, their impact on system performance. System-level issues, general planning, design, information analysis, problem paradigms. How to frame problems. Techniques to combine engineering and information technology. prereq: 8541, college-level computer programming course
IE 8541 - Decision Support Systems
Credits: 4.0 [max 4.0]
Course Equivalencies: HUMF 8541/IE 8541
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Decision Support Systems (DSSs) to assist people in making better decisions, interpreting complex information, and managing complex situations safely/effectively. Principles of human-centered design, cognitive engineering, and evaluation. Applications in projects of students' own choosing.
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems
Credits: 4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Cutting edge research issues in production, inventory, distribution systems. Stochastic models of manufacturing systems, stochastic inventory theory, multi-echelon inventory systems/supply chains, supplier-retailer/supplier-manufacturer coordination, supplier/warehouse networks, business logistics, transportation. prereq: 5551
IE 8794 - Industrial Engineering Research
Credits: 1.0 -6.0 [max 10.0]
Typically offered: Every Fall, Spring & Summer
Directed research. prereq: instr consent
IE 8888 - Thesis Credit: Doctoral
Credits: 1.0 -24.0 [max 100.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
(No description) prereq: Max 18 cr per semester or summer; 24 cr required