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IEE 475: Simulating Stochastic Systems

Theodore P. Pavlic

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

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Lecture D1 (2022-09-15): Probability and Random...

IEE 475: Simulating Stochastic Systems ·

2022/09/15

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 probability density as mass density, and a mean as a center of mass). This allows us to discuss the "statistical leverage" of outliers in a distribution (i.e., although they happen infrequently, they still have the

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Lecture C2 (2022-09-13): Beyond DES Simulation – SDM,...

IEE 475: Simulating Stochastic Systems ·

2022/09/14

In this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences, and engineering (Multi-Agent Systems, MAS)/operations research. NetLogo is introduced, and it is used to present examples of running ABM's as well as the code behind them. At the end of the ABM/NetLogo

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Lecture C1 (2022-09-08): Basic Simulation Tools and...

IEE 475: Simulating Stochastic Systems ·

2022/09/08

In this lecture, we discuss different approaches to implementing Discrete Event System (DES) simulations (DESS) with simple spreadsheets (e.g., Microsoft Excel, Google Sheets, Apple Numbers, etc.). We cover inventory management problems (such as the newsvendor model) as well as Monte Carlo sampling and stochastic activity networks (SAN's). Although we show that spreadsheets can be very powerful for this kind of work, we highlight that this approach is cumbersome for systems with increasing

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Lecture B3 (2022-09-06): DES Examples, Part II, plus...

IEE 475: Simulating Stochastic Systems ·

2022/09/06

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 involves defining relationships ACROSS TIME that allow the spreadsheet to (in a declarative fashion) reconstruct the trajectory that is the output of the simulation. We then pivot to discussing the previous

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Lecture B2 (2022-09-01): DES Examples, Part I

IEE 475: Simulating Stochastic Systems ·

2022/09/01

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.

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Lecture B1 (2022-08-30): Fundamental Concepts of...

IEE 475: Simulating Stochastic Systems ·

2022/08/30

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

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Lecture A2 (2022-08-25): Introduction to Simulation...

IEE 475: Simulating Stochastic Systems ·

2022/08/25

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.

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Lecture A1 (2022-08-23): Introduction to Modeling

IEE 475: Simulating Stochastic Systems ·

2022/08/23

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

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Lecture 0 (2022-08-18): Course Introduction

IEE 475: Simulating Stochastic Systems ·

2022/08/18

In this lecture, we go over course policies for the Fall 2022 session of IEE 475.

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IEE 475: Simulating Stochastic Systems

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