SIMULATION MODELING LABORATORY

Prerequisites & course information

Requisites: Concurrent enrollment in I SY E 320 Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Apply knowledge of math, science, and engineering principles to model real stochastic systems Audience: Undergraduate 2. Identify, formulate, and solve problems using appropriate data analytic and simulation approaches Audience: Undergraduate 3. Understand and apply probabilistic modeling techniques such as Markov Chains and queueing theory to study stochastic systems Audience: Undergraduate 4. Apply statistical methods to analyze stochastic behaviors of the systems Audience: Undergraduate 5. Apply simulation software to model the process and evaluate performance measures of the systems Audience: Undergraduate Learning Outcomes: 1. Apply knowledge of math, science, and engineering principles to model real stochastic systems Audience: Undergraduate 2. Identify, formulate, and solve problems using appropriate data analytic and simulation approaches Audience: Undergraduate 3. Understand and apply probabilistic modeling techniques such as Markov Chains and queueing theory to study stochastic systems Audience: Undergraduate 4. Apply statistical methods to analyze stochastic behaviors of the systems Audience: Undergraduate 5. Apply simulation software to model the process and evaluate performance measures of the systems Audience: Undergraduate