In this lecture, we cover fundamentals of discrete-event system (DES) simulation (DESS). This involves reviewing basic simulation concepts (entities, resources, attributes, events, activities, delays) and introducing the event-scheduling world view, which provides a causality framework on which an automatic simulation of a DES system can be built. We also discuss briefly how the stochastic modeling inherent to DESS means that outputs will be variable and thus will require rigorous statistics to make sense of.
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Tuesday, August 30, 2022
Thursday, August 25, 2022
Lecture A2 (2022-08-25): Introduction to Simulation Modeling
In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete event system simulation) and then focus on how stochastic modeling is used within discrete-event system simulation.
Tuesday, August 23, 2022
Lecture A1 (2022-08-23): Introduction to Modeling
In this lecture, we introduce Industrial and Systems Engineering as a blend of science and engineering that necessitates model building. We then define model (as something that answers a "What If" question) and different types of models. This gives us an opportunity to discuss how modeling is less about describing reality and more about generating tools to do useful things/make useful predictions. We end with a comparison of mental and quantitative models, as well as a comparison of different types of quantitative models (including simulation modeling).
Thursday, August 18, 2022
Lecture 0 (2022-08-18): Course Introduction
In this lecture, we go over course policies for the Fall 2022 session of IEE 475.
Popular Posts
-
In this lecture, we go over course policies for the Fall 2022 session of IEE 475.
-
In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete eve...
-
In this lecture, we wrap up the course content in IEE 475. We first do a quick overview of the four variance reduction techniques (VRT's...
-
This lecture section is a cumulative review of material from the semester and is meant to serve as a study guide for students preparing for ...
-
In this lecture, we continue to discuss hypothesis testing -- introducing parametric, non-parametric, exact, and non-exact tests and reviewi...
-
Today's lecture covers the basics of probability (including introduction to measure spaces) and random variables. We also go over some r...
-
This lecture continues to discuss issues related to estimating absolute performance from transient and steady-state simulations (of terminat...
-
In this lecture, we (nearly) finish our coverage of Input Modeling, where the focus of this lecture is on parameter estimation and assessing...
-
In this lecture, we prepare for the final exam and give a brief review of all topics from the course.
-
In this lecture, we review four different Variance Reduction Techniques (VRT's). Namely, we discuss common random numbers (CRNs), contro...