Course SummaryProspective Students
Course Summary
(3 Credits) Examination of the use of stochastic simulation approaches to modeling and their application to systems engineering.
Overview
The scale and cost of typical systems engineering projects mandate that proposed solutions are explored through integrated models and simulations that appropriately account for uncertainties such that stakeholders are confident that the system will work as intended upon deployment. In this course, we examine the use of stochastic simulation approaches to these integrated models and their application to systems engineering. The course covers fundamental concepts, methods, and applications of modeling and simulation as well as modeling systems of interconnected heterogeneous systems using hybrid simulation. The course begins with an overview of different types of systems and models, sources of randomness and uncertainty, and reviews basic concepts related to computer simulation. Students are given an overview of two types of stochastic simulation, namely Monte Carlo simulation and discrete event simulation. Basic concepts related to input modeling, experimentation, output analysis, model verification and validation, and simulation-based optimization are covered. Students will then learn how to combine the two simulation techniques to develop hybrid simulations.
Prerequisites
The course has no formal prerequisites. However, recommended preparation includes basic:
- Mathematics
- Knowledge of probability and statistics (e.g, undergraduate statistics equivalent to STAT 200)
- Use of Excel
- Coding (in any programming language or generic tool/package such as MATLAB)
The most important prerequisite of all is your motivation and commitment to learning.
Course Objectives
After successful completion of this course, the student will be able to:
- Determine an appropriate type of simulation model to properly account for stochasticity and uncertainty in a system.
- Build and apply Monte Carlo Simulation (MCS), Discrete-Event Simulation (DES), and hybrid MCS-DES models to solve systems engineering problems.
- Use real-world data to estimate and model input parameters of stochastic simulation models.
- Estimate and interpret different types of output statistics generated by simulation models and make statistically valid decisions.
- Develop and apply simulation-based optimization techniques and metamodels to find near-optimal solutions for large-scale stochastic optimization problems.
Course Materials
Required Textbook
- Simio and Simulation: Modeling, Analysis, Applications, 6th Edition (2023) by Jeffrey S. Smith, David T. Sturrock, and W. David Kelton.
(ISBN: 978-1-938207-03-7 (E-book) ISBN: 979-8-802969-55-7 (Printed book)
E-Book Option: A free online version is available for this textbook at https://textbook.simio.com/SASMAA/index.html
Required Software
- Simio Simulation Software – Instructions for downloading and activating student licenses will be provided by the instructor (the instructor will be point of contact for all software-related issues).
- Microsoft Office Suite (Excel and Word)
- Minitab (see instructions in the Modules tab on how to get access to Minitab)
Optional Texts and Other Resources
- Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol, Discrete-Event System Simulation, 4th Edition, Prentice Hall, Upper Saddle River, NJ, 2005. ISBN 13: 9780131446793
- Law, A. M., Simulation Modeling and Analysis, 4th Edition, McGraw-Hill, Boston, MA, 2007. ASIN: B011DBA1YO
- Seila, A. F., V. Ceric, and P. Tadikamalla, Applied Simulation Modeling, Duxbury – Brooks/Cole, Belmont, CA, 2003. ISBN 13: 9780534381592
- Nelson, B. L., Foundations and Methods of Stochastic Simulation: A First Course, Springer Science & Business Media, 2013. ISBN 13: 978-1461461593
Grading and Examinations
| Assignment | Points Each | Quantity | Percentage of Final Grade |
| Weekly Assignments | 100 each | 6 | 72% |
| Final Exam | 100 | 1 | 28% |
Grades will be based on the following scale:
A = 95 – 100, A- = 90 – 94, B+ = 87 – 89, B = 84 – 86, B- = 80 – 83, C+ = 77 – 79, C = 70 – 76, D = 65 – 69, and F = 65 and below.
Written Assignments
There are six weekly assignments (one for each of the weeks 1-6). The assignment for each week will be released at the beginning of the week and must be submitted to the appropriate Canvas assignment before 11:59 PM EST on Sunday in that week for the submission to be considered “on-time” (see the course schedule for exact due dates). Details for assignment are as follows:
- All homework assignments are individual assignments.
- Most assignments involve one or two video labs. The video labs are an important part of learning in this class. The video labs focus on learning and implementation of models in the simulation software that we use in this class. The lessons, on the other hand, don’t teach you the software, but rather focus on the concepts/theories.
- Each video lab involves watching a set of videos that walk you through the steps for developing and analyzing a simulation model.
- Detailed submission instructions (number and type of files to submit, formatting, etc.) are provided in each assignment’s page on Canvas.
- It is crucial that you start working on the video labs/assignments early. If you wait until Sunday to start working on your assignment, it is highly likely that you will not able to finish your assignments before the deadline.
Exam
There will be one final exam given in Week 7. The exam is released at the beginning of Week 7 and is due at 11:59 PM EST on Sunday of that week. There will be no separate homework assignment for week 7 due to the final exam..
Course Topics
- Introduction to Simulation, Random Variables, and Queueing Theory
- Monte Carlo and Discrete Event Simulation
- Output Analysis and Decision-Making
- Input Modeling and Analysis
- Simulation-Based Optimization
- Advanced Topics – Simulation Metamodeling and Input Uncertainty
- Hybrid Simulation
University Policies
Please view the University Policies and Resources which includes important information regarding academic integrity, student disability resources, educational equity, counseling services, and technical requirements.
Prospective Students
For more information on this program, check out the Master of Systems Engineering website!