Industrial Engineering (ENGI)
Introduction to the principles of time value of money, analysis of investments, break-even concepts, risk analysis, alternatives analysis, tax implications, certainty and uncertainty.
Offered: Resident and Online
Introduction to manufacturing and production processes. Topics include production process as a human/machine system, planning, organizing, designing, and operating production systems.
Offered: Resident and Online
Placement in a manufacturing plant, hospital, library, police department, or similar location, or related organization for a controlled learning experience within the student's career specialization area. Application procedures processed through the Career Center. Must apply semester prior to internship.
Registration Restrictions: Sophomore status, 2.00 GPA, two courses in major, declared major, not more than one CSER behind
Offered: Resident
Prerequisite: ENGR 210 and First Year Engineering Gate with a score of 5
Advanced forecasting and data modeling methods and techniques.
Note: First Year Engineering Gate courses consistent of: MATH 131, MATH 132, PHYS 231, and ENGR 110 (or ENGR 115) with a grade of ‘C’ or higher
Offered: Resident
Prerequisite: ENGR 210
Revealing business and economic patterns and information hidden in data by transforming data using algebraic and statistical methods.
Offered: Resident
Online Prerequisite: ENGR 210
Revealing business and economic patterns and information hidden in data by transforming data using algebraic and statistical methods. Enabling computers to learn to predict and categorize events by using data.
Offered: Online
Online Prerequisite: ENGR 210
Introduction to basic principles and application of deterministic analytical methods. Topics include linear programming, dynamic programming, nonlinear optimization, and genetic algorithms.
Offered: Online
Prerequisite: ENGI 230
Introduction to the design, analysis and selection of manufacturing facilities and material handling equipment. Topics include integration of computer systems, material flow and storage, and economic implications.
Offered: Resident and Online
Introduction to basic principles and application of deterministic analytical methods. Topics include linear programming, integer programming, dynamic programming and nonlinear optimization.
Offered: Resident
Introduction to decision-making modeling and analysis subject to randomness, uncertainty, and risk. Topics include stochastic dynamic programming, Markov chains, and queuing theory.
Registration Restrictions: Junior status
Offered: Resident
Prerequisite: First Year Engineering Gate with a score of 5
Introduction to information systems used in the analysis, design, and management of complex engineering projects. Topics include identifying potential data anomalies and methods for ameliorating these problems.
Note: First Year Engineering Gate courses consistent of: MATH 131, MATH 132, PHYS 231, and ENGR 110 (or ENGR 115) with a grade of ‘C’ or higher
Offered: Resident
Online Prerequisite: ENGR 210
Six Sigma is a process improvement methodology that is important for quality management/improvement across a wide range of industries. It a method that provides organizations tools and methods to improve the capability of their business processes by reducing variation while improving performance and profitability. Six Sigma places a strong emphasis on statistics and rigorous data analysis and the implementation process (DMAIC). Lean methods of process improvement are closely related to Six Sigma, and are often combined as “Lean Six Sigma.” These methodologies rely on statistics-driven approaches to quality management, lean approaches focus on eliminating waste and non-value added processes, while traditional Six Sigma emphasis on reducing variation and improving control. In practice, these techniques are often interrelated and complement each other, as both work towards the goal of delivering the highest level of quality at the lowest cost.
Offered: Online
Machine learning introduces the methods that are used to provide computers the ability to perform various levels of artificial intelligence (AI) with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs and algorithms as well as the underlying data requirements that can enable computers to teach themselves, self-organize objects, and to grow or change when exposed to new data or sensory information.
Offered: Resident
This course introduces the fundamentals of Computer Numerical Control (CNC) for machining and Programmable Logic Chips (PLC) for factory control. Specifically, the course teaches the basic elements and tools of PLC necessary to create a complete program using ladder logic common to most platforms. The CNC aspects of the course focus on setup and programming of CNC machining and turning centers to include programming these tools on a variety of brands and machines.
Offered: Online
A first course in decision analysis that extends the domain of decision-making problems from those considered in traditional statistical hypothesis testing scenarios: modeling decisions, where the emphasis is on structuring decision problems using techniques such as influence diagrams and decision trees, modeling uncertainty, which covers subjective probability assessment, use of classical probability models, Bayesian analysis, and value of information, and modeling preferences, which introduces concepts of risk preference, expected utility, and multi-attribute value and utility models.
Offered: Resident
Resident Prerequisite: ((RSCH 201 or Inquiry Research with a score of 80 or Research Assessment with a score of 80 or Research (prior to 2017-2018) with a score of 80) and ENGR 210 and First Year Engineering Gate with a score of 5 )
Online Prerequisite: ENGR 210 and First Year Engineering Gate with a score of 5
Human biological and psychological capabilities and limitations in the industrial setting. Topics include techniques and methods for applying the principles of human factors engineering and ergonomics to systems design.
Offered: Resident and Online
Prerequisite: ENGI 450
This course will teach students the principles of industrial ergonomics. It focuses on physical ergonomics and ergonomic assessment tools. At the end of the course students will have used the most common ergonomic assessment tools and will be able to assess the risk of musculoskeletal injuries.
Offered: Resident
Prerequisite: MATH 334
Introduction to the structure, logic and methodologies of systems simulation. Topics include the generation of random numbers, simulation languages, and simulation models and analysis.
Offered: Resident
Capstone (part 1) emphasizes the planning and project management process, from inception to completion. In addition to technical design, factors such as safety, economics and ethical and societal implications are considered.
Registration Restrictions: Must be taken in the last semester of enrollment
Offered: Online
Online Prerequisite: ENGI 461
Capstone (part 2) emphasizes the execution and implementation of the project process for an approved project. Students put into motion an approved project plan.
Note: Can be taken in the same semester as ENGI 461
Offered: Online
Selected topics in various areas of Industrial and Systems Engineering. May be repeated for credit when topic varies.
Offered: Resident
Placement in a manufacturing plant, hospital, library, police department, or similar location or related organization for a controlled learning experience within the student's career specialization area. Applications are processed through the department Faculty Intern Advisor. Applicants must apply the semester prior to starting the internship.
Registration Restrictions: Junior or Senior status
Offered: Resident