PODCAST · education
IEE 475: Simulating Stochastic Systems
by Theodore P. Pavlic
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.
Episodes
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Lecture G1 (2026-10-15): 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 to match statistical distributions in data. Over this unit, we cover collection of data for this...
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Lecture F (2026-10-01): 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 can focus on the topics that students feel like they need the most help with.
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Lecture E2 (2026-09-29): 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 important properties of a pseudo-random number generator (PRNG), uniformity and independence, and...
-
Lecture E1 (2026-09-04): Random-Number Generation
In this lecture, we first cover some discrete distributions (and the Poisson process) that we ran out of time for during the previous lecture. We then launch into a discussion of how to generate pseudo-random numbers distributed uniformly between 0 and 1 (which are necessary for us to easily...
-
Lecture D2 (2026-09-22): Probabilistic Models
In this lecture, we review basic probability fundamentals (measure spaces, probability measures, random variables, probability density functions, probability mass functions, cumulative distribution functions, moments, mean/expected value/center of mass, standard deviation, variance), and then we...
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Lecture D1 (2026-09-17): Probability and Random Variables
In this lecture, we introduce the measure-theoretic concept of a random variable (which is neither random nor a variable) and related terms, such as outcomes, events, probability measures, moments, means, etc. Throughout the lecture, we use the metaphor of probability as mass (and thus…
more -
Lecture C2 (2026-09-15): Beyond DES Simulation – SDM, ABM, and NetLogo
This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other simulation modeling approaches that contrast with Discrete Event System (DES) simulation. In particular, in this lecture, we briefly introduce System…
more -
Lecture C1 (2026-09-10): Basic Simulation Tools and Techniques
This lecture covers content related to implementing simulations with spreadsheets and the motivations for the use of special-purpose Discrete Event System Simulation tools. In particular, we discuss different approaches to implementing Discrete Event System (DES) simulations (DESS) with simple...
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Lecture B3 (2026-09-08): Discrete-Event Simulation Examples, Part II
In this lecture, we close out our review of DES fundamentals and hand simulation. After going through a hand-simulation example one last time, we show how to implement a Discrete Event System (DES) simulation using a spreadsheet tool like Microsoft Excel without any "macros" (VBA, etc.). This...
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Lecture B2 (2026-09-03): Discrete-Event Simulation Examples, Part I
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…
more -
Lecture B1 (2026-09-01): 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) and introducing the event-scheduling world view, which provides a causality framework on which…
more -
Lecture A2 (2026-08-27): 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. In particular, we define terms such as…
more -
Lecture A1 (2026-08-25): 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…
more -
Lecture 0 (2026-08-20): Introduction to the Course and Its Policies
This short lecture introduces the IEE 475 course (Simulating Stochastic Systems) for the Fall 2026 semester. Due to issues with the projector and the camera in the room, the video feed only shows the slides that are being shared (but there is audio that narrates them, and any pointing to the…
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Lecture M (2025-12-02): Final Exam Review
In this lecture, we prepare for the final exam and give a brief review of all topics from the course.
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Lecture K2 (2025-11-25): Variance Reduction Techniques, Part 2 (Antithetic Variates 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. Each one of these is a different approach to reducing the variance in the estimation of relative…
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Lecture K1 (2025-11-20): 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 estimation of performance for a single simulation model. We then move to different ways to use confidence...
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Lecture J4 (2025-11-19): 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 to motivate the problem of relative performance estimation. We introduce two-sample confidence...
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Lecture J3 (2025-11-13): 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 many replications are needed in a simulation study (focusing first on transient simulations of...
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Lecture J2 (2025-11-06): 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 starts by overviewing point estimation, bias, and different types of point estimators. This includes...
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Lecture J1 (2025-11-04): 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 simulation outputs. Most of this lecture is a review of statistics and reasons for the assumptions…
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Lecture I (2025-10-30): 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 and false negative rate) and the connection to statistical power (sensitivity). We review the…
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Lecture H (2025-10-28): Verification, Validation, and Calibration of Simulation Models
At the start of this lecture, we review statistical topics and fitting techniques from Unit G (particularly Lecture G3, on goodness of fit). In particular, we review hypothesis testing fundamentals (type-I error, type-II error, statistical power, sensitivity, false positive rate, true negative...
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Lecture G3 (2025-10-23) 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 then briefly review fundamentals of hypothesis testing. We discuss type-I error, p-values, type-II...
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IEE 475: Lecture G2 (2025-10-21): 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 distributions that may be a good fit for data. We start with an example where a histogram leads us to...
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Lecture G1 (2025-10-16): 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 to match statistical distributions in data. Over this unit, we cover collection of data for this...
-
Lecture F (2025-10-02): Midterm Review for IEE 475 (Simulating Stochastic Systems)
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 can focus on the topics that students feel like they need the most help with.
-
Lecture E2 (2025-09-30): 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 important properties of a pseudo-random number generator (PRNG), uniformity and independence, and...
-
Lecture E1 (2025-09-25): Random-Number Generation
In this lecture, we first cover some discrete distributions (and the Poisson process) that we ran out of time for during the previous lecture. We then launch into a discussion of how to generate pseudo-random numbers distributed uniformly between 0 and 1 (which are necessary for us to easily...
-
Lecture D2 (2025-09-23): Probabilistic Models
In this lecture, we review basic probability fundamentals (measure spaces, probability measures, random variables, probability density functions, probability mass functions, cumulative distribution functions, moments, mean/expected value/center of mass, standard deviation, variance), and then we...
-
Lecture D1 (2025-09-18): Probability and Random Variables
In this lecture, we introduce the measure-theoretic concept of a random variable (which is neither random nor a variable) and related terms, such as outcomes, events, probability measures, moments, means, etc. Throughout the lecture, we use the metaphor of probability as mass (and thus…
more -
Lecture C2 (2025-09-16): Beyond DES Simulation – SDM, ABM, and NetLogo (and pre-lab discussion for Lab 4 and post-lab discussion for Lab 3)
This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other simulation modeling approaches that contrast with Discrete Event System (DES) simulation. In particular, in this lecture, we briefly introduce System…
more -
Lecture C1 (2025-09-11): Basic Simulation Tools and Techniques
This lecture covers content related to implementing simulations with spreadsheets and the motivations for the use of special-purpose Discrete Event System Simulation tools. In particular, we discuss different approaches to implementing Discrete Event System (DES) simulations (DESS) with simple...
-
Lecture B3 (2025-09-09): DES Examples, Part II (and post-lab discussion for Lab 2)
In this lecture, we close out our review of DES fundamentals and hand simulation. After going through a hand-simulation example one last time, we show how to implement a Discrete Event System (DES) simulation using a spreadsheet tool like Microsoft Excel without any "macros" (VBA, etc.). This...
-
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 examples. These examples show how different research/operations questions can lead to different...
-
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) and introducing the event-scheduling world view, which provides a causality framework on which…
more -
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 modeling is used within discrete-event system simulation. In particular, we define terms such as…
more -
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" question) and different types of models. This gives us an opportunity to discuss how modeling is…
more -
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 engineered systems. The lecture covers contents of the syllabus as well as where students can…
more
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ABOUT THIS SHOW
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.
HOSTED BY
Theodore P. Pavlic
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IEE 475: Simulating Stochastic Systems currently has 39 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
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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.
How often does IEE 475: Simulating Stochastic Systems release new episodes?
IEE 475: Simulating Stochastic Systems has 39 episodes. Check the episode list to see recent publication dates and frequency.
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IEE 475: Simulating Stochastic Systems is created and hosted by Theodore P. Pavlic.
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