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) 
  • Engineering Management track (on-campus and online)
  • Executive Health Systems track (Manhattan and online)
  • Health Systems concentration (on-campus and online) 
• Combined 4+1 BS ISE-MS Industrial and Systems Engineering Program for Binghamton undergraduates 

Research Topics

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 Binghamton 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

• SSIE 505 - Applied Probability & Statist.

  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

• SSIE 510 - Enterprise Systems Engineering

  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

• SSIE 520 - Modeling And Simulation

  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

• SSIE 553 - Operations Research

  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

• SSIE 561 - Quality Assurance For Engineer

  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. 

Sample electives

• SSIE 605 - Appl'd Multivar. Data Analysis

  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

• SSIE 615 - Advanced Supply Chains

  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

• SSIE 641 - Adv Topics in Network Science

  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

• SSIE 644 - Found Of Adaptive Optimization

  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

View current course offerings.

PhD in Industrial and Systems Engineering

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