Logistics
| Semester |
Fall 2026 |
| Time & Days |
MW 9:00AM - 10:30AM |
| Location |
E2-2 #1501 |
| Instructor |
Kihyuk Hong (E2-2 #3104) |
| Office Hours |
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| Email |
[email protected] |
| TAs |
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| Textbook |
Introduction to Probability Models, 13th Edition, by Sheldon M. Ross. |
| Lecture Note |
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| Midterm |
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| Final |
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Course Description
This course covers mathematical models and analytical techniques essential for performance evaluation and decision-making in the rational design and operation of engineering systems subject to stochastic variability—such as production and manufacturing systems, computer and communication systems, and service systems. Key topics include the fundamentals of stochastic processes, Poisson processes and arrival process models, Markov chain models, queueing models, reliability models, decision analysis models, Markov decision processes, and stochastic simulation. Students will learn core concepts, modeling methodologies, and analytical tools for these areas. Prerequisite: IE241 or permission of the instructor.
Grading
| Components |
Note |
% |
| Participation |
In-class participation (attendance, polls) |
10% |
| Homework |
Weekly homework sets |
20% |
| Mini Project |
|
10% |
| Midterm Exam |
In-class |
30% |
| Final Exam |
In-class |
30% |
Course Schedule
| No. |
Date |
Lecture Notes |
📹 |
Readings |
| 01 |
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| 02 |
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| 03 |
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| 04 |
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| 05 |
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| 06 |
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| 07 |
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| 08 |
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| 09 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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