IENG 455 Simulation Modeling and Analysis
This course introduces simulation to analyze uncertainty over time in a production system or services using a collection of random variables known as stochastic processes. The systems are analyzed using different probability distributions and mathematical models. An analysis of systems is carried out to design and determine different problems that could arise, to understand bottlenecks and wait time, inventory control, capacity planning, and reliability of the system. In this course students will learn how to simulate real-world scenarios using discrete event simulation methods to optimize complex manufacturing and health care systems.
Prerequisites
4 Undergraduate credits
Effective December 15, 2025 to present
Learning outcomes
General
- Explain the role of different stochastic processes in simulation modeling.
- Construct an appropriate simulation model using different probability distributions and models.
- Analyze the output data of the simulation model.
- Recommend changes based on the output of the simulation model to the management.