IEE 475: Simulating Stochastic Systems
Archived lectures from IEE 475 (Simulating Stochastic System) given by Ted Pavlic at Arizona State University. A course on discrete event system simulation focused on Industrial Engineering undergraduate students or others learning to use good simulation methodologies.
- Update frequency
- every 3 days
- Episodes
- 30
- Years Active
- 2024 - 2025
Lecture B2 (2025-09-04): DES Examples, Part I
In this lecture, we review fundamentals of Discrete Event System (DES) simulation (e.g., entities, resources, activities, processes, delays, attributes) and we run through a number of DES modeling ex…
Thu 04 Sep 2025
Lecture B1 (2025-09-02): Fundamental Concepts of Discrete-Event Simulation
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…
Tue 02 Sep 2025
Lecture A2 (2025-08-28): 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 mode…
Thu 28 Aug 2025
Lecture A1 (2025-08-26): 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" qu…
Tue 26 Aug 2025
Lecture 0 (2025-08-21): Course Introduction
This lecture introduces students to IEE 475 (Simulating Stochastic Systems), a required course for Industrial Engineering majors that covers the design and analysis of simulation models of real-world…
Thu 21 Aug 2025
Lecture M (2024-12-03): Final Exam Review
In this lecture, we prepare for the final exam and give a brief review of all topics from the course. Students are encouraged to bring their own questions so that the focus of the class is on the top…
Wed 04 Dec 2024
Lecture L (2024-11-26) Course Wrap-Up
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) covered in Unit K. That is, we cover: common random numbers (…
Wed 27 Nov 2024
Lecture K2 (2024-11-21): Variance Reduction Techniques, Part 2 (AVs and Importance Sampling)
In this lecture, we review four different Variance Reduction Techniques (VRT's). Namely, we discuss common random numbers (CRNs), control variates, antithetic variates (AVs), and importance sampling.…
Fri 22 Nov 2024
Lecture K1 (2024-11-19): Variance Reduction Techniques, Part 1 (CRNs and Control Variates)
In this lecture, we start by reviewing approaches for absolute and relative performance estimation in stochastic simulation. This begins with a reminder of the use of confidence intervals for estimat…
Wed 20 Nov 2024
Lecture J4 (2024-11-14): Estimation of Relative Performance
In this lecture, we review what we have learned about one-sample confidence intervals (i.e., how to use them as graphical versions of one-sample t-tests) for absolute performance estimation in order …
Fri 15 Nov 2024
Lecture J3 (2024-11-12): Estimation of Absolute Performance, Part III: Non-Terminating Systems/Steady-State Simulations
In this lecture, we start by further reviewing confidence intervals (where they come from and what they mean) and prediction intervals and then use them to motivate a simpler way to determine how man…
Tue 12 Nov 2024
Lecture J2 (2024-11-07): Estimation of Absolute Performance, Part II: Terminating Systems/Transient Simulations
In this lecture, we review estimating absolute performance from simulation, with focus on choosing the number of necessary replications of transient simulations of terminating systems. The lecture st…
Tue 12 Nov 2024
Lecture J1 (2024-11-05): Estimation of Absolute Performance, Part I: Introduction to Point and Interval Estimation
In this lecture, we introduce the estimation of absolute performance measures in simulation – effectively shifting our focus from validating input models to validating and making inferences about sim…
Tue 05 Nov 2024
Lecture I (2024-10-31): Statistical Reflections
In this lecture, we review statistical fundamentals – such as the origins of the t-test, the meaning of type-I and type-II error (and alternative terminology for both, such as false positive rate an…
Tue 05 Nov 2024
Lecture H (2024-10-29): Verification, Validation, and Calibration of Simulation Models
During this lecture slot, we start with slides from Lecture G3 (on goodness of fit) that were missed during the previous lecture due to timing. In particular, we review hypothesis testing fundamental…
Wed 30 Oct 2024
Lecture G3 (2024-10-24): Input Modeling, Part 3: Parameter Estimation and Goodness of Fit
In this lecture, we (nearly) finish our coverage of Input Modeling, where the focus of this lecture is on parameter estimation and assessing goodness of fit. We review input modeling in general and t…
Fri 25 Oct 2024
Lecture G2 (2024-10-22): Input Modeling, Part 2: Selection of Model Structure
In this lecture, we continue discussing the choice of input models in stochastic simulation. Here, we pivot from talking about data collection to selection of the broad family of probabilistic distri…
Tue 22 Oct 2024
Lecture G1 (2024-10-17): Input Modeling, Part 1: Data Collection
In this lecture, we introduce the detailed process of input modeling. Input models are probabilistic models that introduce variation in simulation models of systems. Those input models must be chosen…
Fri 18 Oct 2024
Lecture F (2024-10-03): Midterm Review
During this lecture, we review the topics covered up to this point in the course as preparation for the upcoming midterm exam. Students are encouraged to bring their own questions to class so that we…
Thu 03 Oct 2024
Lecture E2 (2024-10-01): Random-Variate Generation
In this lecture, we review pseudo-random number generation and then introduce random-variate generation by way of inverse-transform sampling. In particular, we start with a review of the two most imp…
Tue 01 Oct 2024
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