ISyE 320: Simulation and Probabilistic Modeling

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ISyE 320 - Simulation and Probabilistic Modeling

Catalog Description

320 Simulation and Probabilistic Modeling. I or II or SS; 3 cr. Analysis of stochastic systems using both analytic methods and computer simulation. Empirical and theoretical models of arrival and service processes. State spaces and state transition probabilities. Simulation of queuing and manufacturing systems. Continuous time Markov analysis of manufacturing systems. P: Stat 311 or equiv.

Course Prerequisite(s)

IE313
Stat311 and Stat312
Math320 or Math340

Prerequisite knowledge and/or skills

Knowledge of probability and statistics
Knowledge of linear algebra

Textbook(s) and/or other required material

Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D. M., Discrete-Event System Simulation, Prentice-Hall, New Jersey, 2001

Course objectives

Understand the stochastic nature of real systems
Learn modeling techniques and tools for stochastic systems
Be able to use simulation software tools to model a system and to estimate performance measures of the system
Be able to present the results of the modeling process to users in a non-technical presentation

Topics covered

Poisson arrival process
Markov chain and Markov processes
Queueing systems
Simulation of stochastic processes

Class/laboratory schedule

Lecture: two 75-minute periods per week
Lab: one 50-minute period per week

Contribution of course to meeting the professional component

This course contributes primarily to the students' knowledge of engineering topics, but does not provide design experience.

The following statement indicates which of the following considerations are included in this course: economic, environmental, ethical, political, societal, health and safety, manufacturability, sustainability.

Economic considerations are a primary concern of the course

Relationship of course to undergraduate degree program objectives and outcomes

This course primarily serves students in the department. The information below describes how the course contributes to the undergraduate program objectives.

This course helps students to understand the process of modeling and analysis of dynamic systems that are subject to uncertainty, including manufacturing, service, communication, computer and health-care systems. Students will gain hands-on experience in the use of simuation software and modeling tools such as Arena and spreadsheets.

Assessment of student progress toward course objectives

Homework: Weekly or bi-weekly assignments
Three in-class exams of 75-minutes each
One course project

Person(s) who prepared this description

Leyuan Shi

Copyright 2007 The Board of Regents of the University of Wisconsin System
Date last modified: 28-Dec-2007
Content by: prpeters@engr.wisc.edu
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