10 courses with the subject IE, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
IE 215Human Factors Lab1
This laboratory course provides students with practical experience in applying human factors principles and ergonomics to real-world systems. Students will conduct experiments, perform data analysis, and assess human performance in various work environments and tasks. Emphasis is placed on understanding human limitations, capabilities, and designing systems that optimize human performance and safety. (1 credit; 3 contact hours)
This course provides an overview of industrial engineering principles, practices, and methodologies. Students will learn about the roles of industrial engineers in various sectors, key tools and techniques used in the field, and the importance of optimizing systems and processes for effectiveness and efficiency. (2 credits; 2 contact hours, 2 Lecture)
This course introduces the fundamental concepts and techniques of deterministic operations research. Students will learn to model and solve optimization problems using linear programming, integer programming, network flows, and other deterministic methods. Emphasis will be placed on formulating problems, utilizing computational tools, and interpreting results. (3 credits; 3 contact hours, 3 Lecture)
This course covers the fundamental concepts and methodologies in facilities planning and design. Students will learn to analyze facility needs, develop layout designs, and evaluate the impact of facility decisions on operations. Emphasis will be placed on efficiency, safety, and sustainability in facility design. (3 credits; 3 contact hours, 3 Lecture)
This course introduces students to the principles and techniques of experimental design. Emphasis will be placed on understanding how to design experiments, analyze data, and interpret results in various contexts, including industrial applications, quality control, and research. Software utilized may include Minitab and R. (3 credits; 3 contact hours, 3 Lecture)
Design for X is an engineering and design methodology course that focuses on optimizing product design for various objectives, such as manufacturability, assembly, cost, and reliability. This course introduces students to the principles and methodologies of “Design for Excellence” (DfX), a systematic approach for integrating key product lifecycle considerations into the early stages of design. 4 Lecture/Lab.
This course provides an in-depth understanding of quality control concepts, methodologies, and tools used to ensure product and service quality in various industries. Students will learn statistical quality control, quality improvement techniques, and the role of quality management systems. (3 credits; 3 contact hours, 3 Lecture)
Human Factors Engineering (also known as Ergonomics) focuses on the design and optimization of systems, tasks, and environments and their alignment with human capabilities and limitations. This course introduces students to the principles and techniques used to improve the interaction between humans and systems. Topics include human sensory and cognitive processes, human performance, workstation design, human error, and usability testing in various systems and products, such as tools, machines, and information technology. (3 credits; 3 contact hours, 3 Lecture)
This course provides an introduction to reliability and maintainability principles applied to engineering systems. Topics include the assessment, analysis, and improvement of reliability, availability, and maintainability in systems through various methodologies and models. Students will study reliability functions, failure distributions, reliability models, system reliability, and maintenance strategies, as well as techniques for designing systems for improved reliability and availability. (3 credits; 3 contact hours, 3 Lecture)
IE 222 , ENGR 251 , MATH 256 and MATH 320 . Credits: 3
IE 467AI Decision Support Systems3
This course covers the principles and applications of artificial intelligence (AI) in decision support systems (DSS). Students will learn how AI techniques, such as machine learning, neural networks, fuzzy logic, expert systems, and optimization algorithms, can be integrated into decision-making processes. The course will focus on practical applications in industrial engineering, operations research, and management systems. (3 credits; 3 contact hours, 3 Lecture)
