IEE 475: Simulating Stochastic Systems

Theodore P. Pavlic
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Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic

  1. 2天前

    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 choices of entities/resources/etc. We close with a hand-simulation example of a single-channel, single-server queue with provided interarrival times and service times.

  2. 4天前

    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 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.

  3. 8月28日

    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 system, dynamic system, state, state variable, activity, delay, resource, entity, and the notion of "input modeling."

  4. 8月26日

    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 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).

  5. 8月21日

    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 find more information in the Canvas Learning Management System site for the course.

  6. 2024/12/04

    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 topics that students feel they need the most help with.

  7. 2024/11/27

    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 (CRN's), antithetic variates (AV's), importance sampling, and control variates. We then  remember some general comments about the goal of modeling and commonalities seen across simulation platforms (as well as the different types of simulation platforms in general).

  8. 2024/11/22

    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. Each one of these is a different approach to reducing the variance in the estimation of relative or absolute performance of a simulation model. Variance reduction is an alternative way to increase the power of a simulation that is hopefully less costly than increasing the number of replications.

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Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic

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