Uncertainty Modeling For Industrial Engineering
The course covers models and methods based on probability and statistics for industrial
engineering applications; random variables, expectation, distribution fitting, the reliability of systems, central limit theorem and interval estimates in the context of production and service systems.
To study how a computer can be applied in industrial engineering design.
At the end of this course, the student should be able to:
- characterize uncertainty to perform decision-making under uncertainty,
- apply probability and statistics based methods for analysis and prediction in engineering systems
A student on completing this course is expected to:
- perform decision-making under uncertainty,
- use probability and statistics based methods to analyse and predict engineering systems.