Industrial and Systems Engineering Master’s and PhD Programs
As an industrial and systems engineer, you'll study complex systems and look for simplifying
solutions across all environments and fields of study, including manufacturing, management,
health systems and social sciences.
Degrees offered
Watson College offers its MS and PhD in Industrial and Systems Engineering in person
and online via the EngiNet distance learning service.
PhD in Industrial and Systems Engineering (on-campus and online)
MS in Industrial & Systems Engineering (on-campus and online)
Master of Science in Industrial and Systems Engineering Curriculum
The Master of Science in Industrial and Systems Engineering provides the balance of
theory and practical knowledge for the practice of the profession and/or for advancement
to a doctoral program. In recognition of the high concentration of industry in the
ßÙßÇÂþ» area, this program has been structured to serve both full- and part-time
graduate students. Taking advantage of this industrial resource allows the program
to develop a realistic approach to integrating both engineering and non-engineering
systems, such as those found in manufacturing, healthcare, supply chain management
and transportation. This degree program is available in person and fully online.
Required courses
Students must complete four required courses while maintaining at least a B average.
Students will choose between SSIE 553 and SSIE 561
Basic concepts in probability and statistics required in the modeling of random
processes and uncertainty. Bayes' formula, Bayesian statistics, independent events;
random variables and their descriptive statistics; distribution functions; Bernoulli,
Binomial, Hypergeometric, Poisson, normal, exponential, gamma, Weibull and multinomial
distributions; Chebyshev's theorem; central limit theorem; joint distributions;
sampling distributions; point estimation; confidence intervals; student-t, x squared
and F distributions; hypothesis testing; contingency tables, goodness of fit, non-parametric
statistics, regression and correlation. Prerequisite: one year of calculus. Term offered
varies. 3 credits.
Levels: Graduate, Undergraduate
Global competition is serving as a catalyst for continuous process improvement
and the methodical enhancement of system-wide efficiencies. This is true in disciplines
ranging from the medical arena and service related systems to manufacturing. The underlying
science that contributes to the systematic analysis of complex enterprise-wide systems
is the focus of this course. Concepts that can be used in a synergistic manner to
enhance an enterprise's efficiency and profitability will be addressed. Prerequisite: Graduate
standing or permission of instructor. Term offered varies. 3 credits.
Levels: Graduate, Undergraduate
Stochastic processes, review of probability and statistics, covariance, input data
selection, random number generators, non-parametric tests for randomness, generation
of random variates, output data analysis, terminating and non-terminating simulations,
model validation, comparison of alternatives, variance reduction techniques, sensitivity
analysis, experimental design and predictive models. Prerequisite: SSIE 505 or equivalent.
Term offered varies. 3 credits.
Levels: Graduate, Undergraduate
Operations research (OR) is devoted to the determination of the best course of
action of a decision problem, given resource restrictions. Course provides the engineer
with a firm grounding in the use of OR (mathematical) techniques devoted to the modeling
and analysis of decision problems. Techniques include the following: decision modeling;
linear, integer and dynamic programming; emerging optimization techniques (e.g., genetic
algorithms, simulated annealing, etc.); game theory; and queueing theory. Problem areas include the following: transportation models;
project/production scheduling; inventory models; assignment problems. Prerequisite:
Graduate standing or permission of instructor. Term offered varies. 3 credits.
Levels: Graduate, Undergraduate
Statistical quality control, designing for quality, process control, vendor and
customer quality issues, quality costs and production. Prerequisites: SSIE 505 or
permission of instructor. Offered in the Spring semester. 3 credits.
Levels: Graduate, Undergraduate
Degree-completion options
Thesis: Four additional graduate-level elective courses (at least one at 600-level), plus
6 credits of thesis work followed by oral presentation and defense.
Project: Five additional graduate-level elective courses (at least one at 600-level), plus
a project of at least 3 credits followed by oral presentation and defense.
Coursework only: Six additional graduate-level elective courses, including at least one 600-level
course that contains project-based coursework to serve as capstone for the termination
requirement.
Course introduces different multivariate data analysis and modeling tools, which
can be used for simultaneously analyzing data with multiple dependent variables. It
is designed to emphasize applied methodologies and applications in multivariate data
analysis, especially in engineering fields. Topics to be covered include: multivariate
regression, logistic regression, multivariate analysis of variance (MANOVA), principal
components analysis, cluster analysis, canonical correlation, factor analysis, and
discriminant analysis. The effective use of advanced data analysis software, such
as SAS, for solving real-world engineering problems will also be addressed. Prerequisite:
SSIE 505 or its equivalent. Term offered varies. 3 credits.
Levels: Graduate, Undergraduate
Course deals with modeling of supply chains, concentrating on production and operations.
Quantitative models will be developed and analyzed to study the benefits of information
sharing, joint planning, and coordination among the various components of a supply
chain. Strategic uses of information and various strategies of supply chains, like
appropriate contacts, component commonality, and postponement will be covered. The
material will also include service industry, and designing and managing globally dispersed
entities. A major activity would be students working in teams, and identifying relevant
problems and developing models to study them. Prerequisite: SSIE 515, and/or SSIE
520 or permission of instructor. Offered in the Fall semester. 3 credits.
Levels: Graduate
This course provides concepts, models, methods and tools developed in the rapidly
advancing field of Network Science. Instructions will be largely based on primary
literature published recently. Topics to be discussed will include: Complex network
topologies, methods for network analysis, visualization and simulation, models of
dynamical/adaptive networks, techniques for mathematical analysis, network stability
and robustness, and applications to social, biological and engineering systems. Prerequisites:
SSIE 523 or permission of the instructor. Students taking this course should have
solid knowledge of linear algebra, probability and statistics, and differential equations.
Term offered varies. 3 credits.
Levels: Graduate, Undergraduate
This course is a survey of the newer, most common adaptive search methods. This is a project- and research-oriented course designed to
give graduate students a foundation from which to explore areas of their own interest.
Focused topics include simulated annealing, genetic algorithms, evolution strategies,
tabu search, ant colony methods, and particle swarm optimization. Other search methods
such as genetic programming, evolutionary programming and random search methods will
be briefly covered. Major emphasis is on NP complete combinatorial problems found
in engineering. Issues such as solution encodings, stochastic convergence, selection
methods, local and global search methods are discussed. Prerequisite: SSIE 505 or
equivalent, SSIE 520, and knowledge of at least one programming language. Offered
in the Fall semester. 3 credits.
Levels: Graduate
The doctoral program in industrial and systems engineering offers a wide variety of
research topics such as optimization, machine learning, human factors/ergonomics,
supply chain management, healthcare systems, enterprise systems, intelligent systems,
data science/analytics, and electronics manufacturing processes, particularly in the
areas of printed circuit-board production and automated assembly. This degree program
is also available fully online.
Degree requirements include:
satisfaction of the learning contract, including proficiency in teaching and residence
requirements
pass a comprehensive exam
presentation of a colloquium on proposed research
acceptance of a prospectus outlining dissertation research
submission of a dissertation, and
defense of a dissertation at oral examination
Sarah S. Lam
Professor, Industrial and Systems Engineering Graduate Director
School of Systems Science and Industrial Engineering
