Ph.D. Curriculum

For students who aspire to conduct research at an academic, industry, or government institution, the University of Minnesota's Ph.D. program in Industrial and Systems Engineering provides the advanced preparation in analytics and operations research needed to succeed. Students in the ISyE Ph.D. program complete challenging and rigorous coursework and conduct research that culminates in a Ph.D. dissertation.

Ph.D. Curriculum

The curriculum of the Ph.D. program includes both coursework and research, and emphasizes theory, methodology, and application of operations research and analytics.

The Ph.D. program enrolls students with backgrounds in applied or pure mathematics, engineering, computer science, statistics, or basic sciences. A student who already holds an MS degree may transfer some course credits in order to shorten his or her program of study, subject to the approval of the student's advisor and the ISyE Director of Graduate Studies. Exceptional students may apply directly to the Ph.D. program without earning an MS degree beforehand.

Ph.D. students are required to take four out of the five core courses listed below. Two of those four must be IE 8521—Optimization and IE 8532—Stochastic Processes and Queueing Systems. Subject to departmental approval, students may replace core courses with more advanced courses if they have already taken the equivalent of the core course elsewhere.

The dissertation research portion of the program is closely supervised by an ISyE faculty advisor of each student's choosing. Research typically focuses on analytics, operations research (optimization and stochastic processes), and/or applications of these methodologies to any of a wide range of contextual areas such as healthcare, supply chain management, transportation, revenue management and innovative marketplaces, financial engineering, and service operations.

The Ph.D. degree requires 44 course credits and 24 thesis credits.

Core Courses for ISyE Ph.D Students

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IE 8521—Optimization

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 Queueing Systems

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: 3521 or equiv

IE 5545—Decision Analysis

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 Control

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 5511—Human Factors and Work Analysis

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

Recommended Electives for ISyE Ph.D. Students (other courses may be used as electives, subject to approval)

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Computer Science

  • CSCI 5304—Computational Aspects of Matrix Theory
  • CSCI 5421—Advanced Algorithms and Data Structures
  • CSCI 5521—Introduction to Machine Learning
  • CSCI 5523—Introduction to Data Mining
  • CSCI 5525—Machine Learning

Applied Economics

  • APEC 8001—Applied Microeconomic Analysis of Consumer Choice and Consumer Demand
  • APEC 8002—Applied Microeconomic Analysis of Production and Choice Under Uncertainty


  • MATH 5615H—Honors: Introduction to Analysis I
  • MATH 5616H—Honors: Introduction to Analysis II
  • MATH 5485—Introduction to Numerical Methods I
  • MATH 5486—Introduction to Numerical Methods II
  • MATH 5654—Prediction and Filtering
  • MATH 8601—Real Analysis I
  • MATH 8602—Real Analysis II 
  • MATH 8651—Theory of Probability Including Measure Theory I 
  • MATH 8652—Theory of Probability Including Measure Theory II


  • STAT 5302—Applied Regression Analysis
  • STAT 5303—Designing Experiments
  • STAT 5401—Applied Multivariate Methods
  • STAT 8051—Advanced Regression Techniques
  • STAT 8101—Theory of Statistics 1
  • STAT 8102—Theory of Statistics 2

Public Health

PUBH 8442—Bayesian Decision Theory and Data Analysis

Industrial and Systems Engineering

  • IE 5561—Analytics and Data Driven Decision Making
  • IE 8531—Discrete Optimization
  • IE 8533—Advanced Stochastic Processes and Queueing Systems
  • IE 8534—Advanced Topics in Operations Research
  • IE 8535—Introduction to Network Science
  • IE 8552—Advanced Topics in Production, Inventory, and Distribution Systems

1st Year Fall Semester 

  • IE 8521—Optimization
  • IE 8532—Stochastic Processes and Queueing Systems 
  • IE 8773—Graduate Seminar 
  • IE Elective or Non-IE Elective

1st Year Spring Semester 

  • IE 5551—Production Planning and Inventory Control
  • IE 5545—Decision Analysis
  • IE 8774—Graduate Seminar 
  • IE Elective or Non-IE Elective

2nd Year Fall Semester 

  • IE Elective (8000 level)
  • IE Elective or Non-IE Elective
  • Non-IE Elective

2nd Year Fall Semester 

  • IE Elective (8000 level)
  • IE Elective or Non-IE Elective
  • Non-IE Elective

Faculty Advisors and Program Timeline

Students typically enter the Ph.D. program without a pre-assigned advisor. During the first year of study, most Ph.D. students focus on coursework and become acquainted with ISyE faculty members and their research areas. By the end of the first year in the Ph.D. program, students should select an advisor.

Ph.D. students should complete about 26 course credits in the first year and take the qualifying exam in the fall semester of the second year. Students should also plan to complete all or almost all course credits (minimum 44, including 12 non-major) by the end of their second year. They should simultaneously work to develop a thesis proposal and take the preliminary exam (the thesis proposal) in the fall semester of their third year.

Years 3 and 4 should be devoted almost entirely to thesis research, with graduation planned sometime in the fourth or fifth year, depending on progress.

The Ph.D. program is designed and intended for full-time students.

Take Advantage of the Twin Cities

UMN-Minneapolis skyline

The Department of Industrial and Systems Engineering is located on the University of Minnesota—Twin Cities campus in the heart of Minneapolis, on the banks of the scenic Mississippi River. A new, modern light rail system connects the campus with downtown Minneapolis, downtown St. Paul, and the MSP International Airport.  With year-round outdoor activities, a metro population of over 3 million, and a diversified economy, the Twin Cities of Minneapolis and St. Paul offer a truly exceptional standard of living. The state of Minnesota is home to 17 Fortune 500 companies.

Ready to apply?

Review our admission requirements and start your application today!


For more information, please contact Graduate Program Coordinator Alyssa Benson at or (612) 625-4909.