MAROUN SEMAAN FACULTY OF ENGINEERING AND ARCHITECTURE DEPARTMENT OF INDUSTRIAL ENGINEERING AND MANAGEMENT COURSE SYLLABUS INDE 504 Discreet Event Simulation Fall 2024 – 2025

General Course Information

Table 1: Overview of Course Information

General Information

Course Number	INDE 504
Course Title	Discreet Event Simulation
Number of Credits	4
Course Level	Undergraduate
Course Format	Face-to-Face
Pre-requisite Courses	INDE 303
Required/Elective	Required
Meeting Schedule
Meeting Location

Course Coordinator, Instructors and Teaching Assistants

Table 2: Contact Information for Course Coordinators, Instructors, and Teaching Assistants

Name	Role (Instructor, Coordinator, TA,)	Office Location	Office Hours	Extension	E-mail
Bacel Maddah

Course Description

Simulation is concerned with developing computer models that mimic the behavior of real systems. This is done through the generation of “random variates” that replicate the statistical properties of a system. Simulation falls under the umbrella of Operations Research. The objective of simulation (like other OR models) is to gain insight into the real system with the ultimate goal of enhancing performance. Simulation is generally used when analytical methods are not effective (i.e., the model cannot be developed with closed-form mathematical expressions). It is also used for validating analytical models. As such, simulation is one of the most popular decision-making tools used by analysts in OR and related disciplines. The objective of this course is to introduce the student to the main ingredients of a successful simulation study. Students will learn how to (i) process input data for a simulation model; (ii) develop a simulation model on a computer; and (iii) analyze the output of the simulation. Simulation models will be developed using the simulation software Arena, Python packages (e.g. SimPy), and @Risk (Excel add-in). Financial, logistic, manufacturing, and service applications will be emphasized.

Course Learning Outcomes

Upon completion of the course, students will be able to:

CLO1. Identify sources of randomness in a system and utilize probability distributions to model them.

CLO2. Develop the successful ingredients of a discrete event simulation.

CLO3. Carry-out input analysis (by hand and software) to fit probability distributions to real data.

CLO4. Understand and analyze the performance of modern random number generators (RNGs).

CLO5. Develop algorithms for generating random varieties based on fitted distributions and RNGs.

CLO6. Conduct statistical analysis of a simulation output.

CLO7. Apply all of the above to design bulletproof system simulations in a process-oriented software environment (Arena).

CLO8. Develop knowledge of Python packages to relate simulation and data science.

CLO9.

CLO10.

CLO11.

CLO12.

CLO13.

CLO14.

CLO15.

Program Learning Outcomes Addressed in The Course

Explicitly indicate which of the program learning outcomes are addressed in the course.

PLO1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.

PLO2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.

PLO3. An ability to communicate effectively with a range of audiences.

PLO4.

PLO5. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.

PLO6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

PLO7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

PLO8.

PLO9.

PLO10.

PLO11.

PLO12.

PLO13.

PLO14.

PLO15.

Course Resources and References

Indicate textbook title, author, and year. Include a link to the Moodle page or the course web page.

Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. Discrete-Event System Simulation, 5th Edition. Prentice-Hall, 2009. (BCNN).Kelton, W. David, Randall P. Sadowski and Nancy Zupick. Simulation with Arena. 6th Edition. McGraw Hill, 2014. (KSZ)Course Webpagehttps://sites.aub.edu.lb/bacel/inde-504/Additional ReferencesLaw, Averill M. Simulation Modeling and Analysis. 5th Edition. McGraw Hill, 2015.Hillier, F. S. and G. J. Lieberman. Introduction to Operations Research, 8th Edition. McGraw-Hill, 2004.Ross, S. M. Introduction to Probability Models, 8th Edition. Academic Press, 2002. Ross, S. M. Simulation, 3rd Edition. Academic Press, 2001.Taha, H. A. Operations Research: An Introduction. 8th Edition. Prentice Hall, 2007.

Course Outline

Include the topics covered and learning activities (assignments, projects, term papers, etc.) with their due dates. Indicate the alignment of topics/ learning activities with the Course Learning Outcomes.

Table 3: Course Schedule Overview

Week	Date	Topic	Learning Activities and Due Dates	Alignment with CLOs
1	Course introduction, OR modeling approach, simulation overview. Review of Probability Theory and Random Variables. A primer on queueing theory. Introduction to Arena. Introduction to Python. (Chs. 1-6 BCNN; Ch 3 KSZ)	CLO1, 2, 7, 8
2	Introduction to discrete event, Monte Carlo, and process-oriented simulation. Hand simulation of a queueing and an inventory system. Introduction to Monte Carlo simulation in @Risk. (Chs. 1-6BCNN)	CLO2
3	Input Analysis. Collecting input probability distributions, estimation of parameters, MLE estimators, goodness of fit tests, Chi-Square, Kolmogorov-Smirnov. Input Analysis in Arena and ExpertFit. Distribution fitting in Python. (Ch 9,BCNN; Ch 4 KSZ; web resources)	CLO1,3,7,8
4	Random number generators. Linear congruential generators (mixed and multiplicative), more general combined generators, tests for random numbers. Modeling basic systems with Arena and SimPy (Python). (Ch 7 BCNN; Ch 4 KSZ; webresources)	CLO4, 7, 8
5	Generating random variates. Methods, inverse transform, convolution, composition methods, acceptance-rejection. Application to well-known distributions and processes, generating from Normal, Gamma and arrival processes. Modeling detailed operations with Arena and SimPy(Python). (Ch 8 BCNN; Ch 5 KSZ; web resources)	CLO5, 7, 8
6	Output analysis. Statistical estimation, confidence intervals, termination rules, variance reduction technique, comparing multiple system designs.Output analysis in Arena. (Chs. 11-12 BCNN, Ch 6 KSZ)	CLO6, 7
7	Class presentation of student projects. (Thursday, November 30 from 9:30 AM to 12:30 PM).	CLO1-8
8	Final Exam (scheduled by the registrar’s office).	CLO1-7
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15

Indicate mode of delivery as Lecture, Recitation, Laboratory, Team-based Learning, etc.

Include all methods for assessing student achievement, including assessment rubrics for evaluating the learning activities.

Grading Criteria

Table 4:Evaluation Criteria and Percentage

Type of Evaluation	Percentage
Assignments*	20%
Lab Exercises	10%
Project**	30%
Final Exam	40
Total	100%

Course Policies

Attendance

For undergraduate courses:

“Students who miss more than one-fifth of the sessions of any course prior to the withdrawal deadline can be withdrawn from the course by the instructor.”

For graduate courses:

“A student who is absent without excuse for more than one third of the number of sessions in any course may be dropped by the instructor of the course”.

Credit Hour Policy

“This course follows AUB Credit Hour Policy for one credit courses. This course meets twice weekly for 4 academic hours per week. The course also includes independent out-of-class assignments for academic hours per week.”

Submissions and Due Dates

Students can communicate with the instructor via email for any additional concern. Answers to reasonable requests should not be expected earlier than 48 business hours from the request time.

Missing Projects/Assignments

Homework will be assigned frequently. It will involve conceptual problems from BCNN book and Arena applications from KSZ book. All students are encouraged to solve the homework problems, and to discuss them with each other and the instructor. However, every student must write and submit the homework assignment individually. the output of Arena or Excel spreadsheets. Certain assignments require uploading Arena files. Doing the homework is the best way to excel in this course. Homework will be assigned frequently. It will involve conceptual problems from BCNN book and Arena applications from KSZ book. All students are encouraged to solve the homework problems, and to discuss them with each other and the instructor. However, every student must write and submit the homework assignment individually. the output of Arena or Excel spreadsheets. Certain assignments require uploading Arena files. Doing the homework is the best way to excel in this course.

Final Examinations:

Please note that students are expected to be on-campus and available to take a final examination for any of their courses at anytime during the final examination period. Students and professors will be notified of the final examination schedule being posted on the Office of the Registrar website as soon as feasible after the change of schedule period (drop and add).

Make-up Quizzes and Exams

A final exam will be scheduled. Students are not allowed to miss an exam except for a valid medical excuse.

Group Work

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Teaching Strategies and Technology Tools

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Use of AI in the Course

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University Rules and Regulations

General Academic Information

Undergraduate Academic Information

Graduate Academic Information

University - Course Withdrawal Policy

A student can withdraw from only one required course per semester. Students who wish to withdraw from more than one required course in any given semester must petition the appropriate faculty committee for permission to do so. Check Withdrawal Information from undergraduate and graduate catalogue.

Academic Integrity (cheating and plagiarism)

Please refer to AUB Student Code of Conduct, in particular section 1.1, which concerns academic misconduct including cheating, plagiarism, in-class disruption, and dishonesty. Please be aware that misconduct is vigorously prosecuted and that AUB has a zero tolerance policy. Course policy is that credible evidence of cheating will result in course failure.

Accessibility Statement to Acknowledge the Unique Learning Needs of Students with Disabilities

AUB strives to make learning experiences as accessible as possible. If you anticipate or experience academic barriers due to a diagnosed disability (including learning disability, mental health, chronic or temporary medical conditions), please contact the Accessible Education Office as soon as possible in order to help establish reasonable accommodations and facilitate a smooth learning process: accessibility@aub.edu.lb; +961-1-350000, x3151; West Hall, 338.

Failure to implement the accommodations provided by the Accessible Education Office constitutes a violation of AUB's non-discrimination policy, and may be reported to the Equal Opportunity Officer/Title IX Coordinator who also serves in the role of Section 504 Coordinator, at 01-350000 ext. 2514, titleix@aub.edu.lb.

Non-Discrimination and Anti-Discriminatory Harassment, including Sexual Harassment at AUB https://www.aub.edu.lb/President/oeo-titleix/Pages/syllabus.aspx

In line with its commitment to the principle of equal opportunity in admission, education, and employment, AUB policies protect you from discrimination and discriminatory harassment, including sex discrimination and sexual harassment, in alignment with the objectives of Title IX of the U.S. Education Amendments of 1972.

The policies are applicable to all the AUB Community including: officers, faculty, staff, academic appointees, students (including medical interns and residents), visiting students, alumni, trainees, visitors, contractors, subcontractors, suppliers, located on campus and at AUB Medical Center, Advancing Research Enabling Communities Center (AREC), or any other facility or program affiliated with the University. The “AUB Community" also includes the dependents and domestic employees of faculty and staff dwelling on campus and at AREC.

If you think you have experienced discrimination, discriminatory harassment, or sexual harassment, we encourage you to inform the Title IX Coordinator, Mitra Tauk at 01-350000 ext. 2514, titleix@aub.edu.lb, report to a Title IX deputy at your faculty or at any other faculty (www.aub.edu.lb/titleix), or report online (www.aub.ethicspoint.com).

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AUB Grading System

https://www.aub.edu.lb/Registrar/Pages/academic-information.aspx

Numeric Course Grade to Letter Course Grade Grade Conversion Chart Table 5:Numeric Course Grade to Letter Course Grade Table 6: Grade Conversion Chart Starting with Numeric Course Grade /100 Corresponding Course Letter Grade Course Letter Grade Quality Points < 60 F A+ 4.3 60 D A 4.0 61–62 D+ A– 3.7 63–65 C– B+ 3.3 66–68 C B 3.0 69–71 C+ B– 2.7 72–74 B– C+ 2.3 75–78 B C 2.0 79–82 B+ C– 1.7 83–86 A– D+ 1.3 87–92 A D 1.0 93–100 A+ F 0.0

Note: The GPA at AUB is capped at 4.0​​

I ​P ​PR ​W NP ​Incomplete ​Pass ​In Progress ​Withdraw ​No Pass