SIMULATION MODELING AND ANALYSIS

Prerequisites & course information

Requisites: ( COMP SCI 200 , 220 , 300 , 301, or placement into COMP SCI 300 ) and ( STAT 312 , 324 , or STAT/MATH 310 ), graduate/professional standing, or member of Engineering Guest Students Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2023 Learning Outcomes: 1. Identify the major capabilities and limitations of discrete-event simulation for modeling types of systems that industrial engineers commonly encounter Audience: Both Grad & Undergrad 2. Model and run discrete-event simulation in practical situations Audience: Both Grad & Undergrad 3. Identify the main assumptions underlying simulation models, and what can happen when these assumptions do not hold Audience: Both Grad & Undergrad 4. Apply the results of the modeling process to management and other non-specialist users of engineering analyses Audience: Both Grad & Undergrad 5. Apply experimental design or data analytics for systems comparison and output analysis of the simulation models Audience: Both Grad & Undergrad 6. Apply simulation input/output techniques to model complicated stochastic systems such as manufacturing production systems and health care systems Audience: Graduate Learning Outcomes: 1. Identify the major capabilities and limitations of discrete-event simulation for modeling types of systems that industrial engineers commonly encounter Audience: Both Grad & Undergrad 2. Model and run discrete-event simulation in practical situations Audience: Both Grad & Undergrad 3. Identify the main assumptions underlying simulation models, and what can happen when these assumptions do not hold Audience: Both Grad & Undergrad 4. Apply the results of the modeling process to management and other non-specialist users of engineering analyses Audience: Both Grad & Undergrad 5. Apply experimental design or data analytics for systems comparison and output analysis of the simulation models Audience: Both Grad & Undergrad 6. Apply simulation input/output techniques to model complicated stochastic systems such as manufacturing production systems and health care systems Audience: Graduate