Twin Cities campus

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Twin Cities Campus

Industrial and Systems Engineering Minor

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: https://cse.umn.edu/isye/
  • Program Type: Graduate minor related to major
  • Requirements for this program are current for Spring 2022
  • Length of program in credits (master's): 6
  • Length of program in credits (doctoral): 12
  • This program does not require summer semesters for timely completion.
The industrial and systems engineering (ISyE) 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
Special Application Requirements:
Students interested in the minor are strongly encouraged to confer with their major field advisor and director of graduate studies, and the Industrial and Systems Engineering director of graduate studies regarding feasibility and requirements.
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Use of 4xxx courses towards program requirements is not permitted.
Courses must be taken on the A-F grade basis, unless only offered S/N. The minimum cumulative GPA for the minor is 3.00.
Minor Coursework (6 to 12 credits)
Master's students select 6 credits, and doctoral students select 12 credits from the following in consultation with the Industrial and Systems Engineering director of graduate studies. Other courses may be chosen with minor director of graduate studies approval.
IE 5511 - Human Factors and Work Analysis (4.0 cr)
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5532 - Stochastic Models (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 8521 - Optimization (4.0 cr)
IE 8531 - Discrete Optimization (4.0 cr)
IE 8532 - Stochastic Processes and Queuing Systems (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 8552 - Advanced Topics in Production, Inventory, and Distribution Systems (4.0 cr)
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Masters
Doctoral
  
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View college catalog(s):
· College of Science and Engineering

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

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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 5531 - Engineering Optimization I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning. prereq: Upper div or grad student or CNR
IE 5532 - Stochastic Models
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to stochastic modeling and stochastic processes. Probability review, random variables, discrete- and continuous-time Markov chains, queueing systems, simulation. Applications to industrial and systems engineering including production and inventory control. prereq: Undergraduate probability and statistics. Familiarity with computer programming in a high level language.
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 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 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 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 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 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