Industrial & Systems Engineering Major (B.S.)
The industrial and systems engineering degree is designed to develop Christ-centered men and women with the values, knowledge, and skills essential to positively influence an industrial setting of complex, integrated systems. The program prepares graduates for the thoughtful integration of work and life and to view the industrial engineering profession as a lifelong commitment to serving others.
Program Educational Objectives
Our goal is, within a few years of graduating, our Industrial and Systems Engineering students will be able to:
- Be recognized as creative thinkers and emerging leaders in engineering; exhibiting an aptitude for continuous improvement.
- Display professional ethics and behavior consistent with foundational Christian principles.
- Be an invested, contributing, core team member with a focus on customers and a sense for business and innovation.
- Be an effective communicator for interfacing with diverse audiences.
- Conduct appropriate checks to produce quality engineering work within acceptable tolerances.
Delivery Format: Residential Only
Introduction to the principles of time value of money, analysis of investments, break-even concepts, risk analysis, alternatives analysis, tax implications, certainty and uncertainty.
Introduction to manufacturing and production processes. Topics include production process as a human/machine system, planning, organizing, designing, and operating production systems.
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
Advanced forecasting and data modeling methods and techniques.
Prerequisite: ENGR 210
Revealing business and economic patterns and information hidden in data by transforming data using algebraic and statistical methods.
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.
Introduction to basic principles and application of deterministic analytical methods. Topics include linear programming, integer programming, dynamic programming and nonlinear optimization.
Introduction to decision-making modeling and analysis subject to randomness, uncertainty, and risk. Topics include stochastic dynamic programming, Markov chains, and queuing theory.
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.
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.
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.
Prerequisite: ENGI 330
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.
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.
Selected topics in various areas of Industrial and Systems Engineering. May be repeated for credit when topic varies.
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