[Department of Technology Management and Innovation]

 [IE 6823 Factory Simulation]

[Fall 2025]

Course Pre-requisites

Students need to have good concepts of probability and statistics, knowledge of Computer literacy, and flow charts and code them.

Course Description

This course provides the student with a solid foundation in discrete simulation; students will be examining modeling and simulation of complex industrial, commercial, and service systems, such as factories and hospitals. Students will develop, run, and test several simulation models during the course. Understand the validation, verification, and calibration of models and test the accuracy of models by testing inputs parameters and outputs performance measures.

 A unique aspect of this course is demonstrating the power of people with different expertise teams to build a suitable model for a given situation. A term project is required that includes detailed analysis and contains materials that are covered during the semester.

Course Objectives 

The course intends to prepare students for understanding and applying simulation modeling techniques for both service and complex manufacturing industries; at the end, the students should be able to:

· understand the general principles and applications underlying the discrete–event simulation theory, how it works, how to interpret results, and how to make a managerial decision from the output results.

· understand the behavior. of systems and demonstrate knowledge to developing a simulation of complex industrial, commercial, and service systems, such as factories and hospitals model, coding and analyzing of this system as well as using simulation software

· understand the advantages and disadvantages of simulation and understand the features and attributes of simulation software.

Course Structure

This course will be delivered via a series of lectures and discussions in Simulation. The course focuses on both manufacturing and services industries. Students are responsible for reading the associated chapters and assigned cases and reviewing key concepts, terms, definitions, discussion questions, and topics. Toward the end of the semester, there will be a team project that focuses on the covered topics.

COURSE MGT Announcements, notes, resources, assignments, schedules, and due dates will be posted to NYU classes.

Readings

The required text for the course is:

Simulation Modeling, and Analysis, Averill M. Law, 6th Edition, McGraw-Hill, 2015, ISBN: 978 – 0 – 0 7 – 340132 – 4.

Discrete – Event System Simulation, 5^th, Editions 2010, Jerry Banks, John S.     Carson II, Barry L. Nelson, and David M. Nicol), Pearson, 

ISBN-10: 0 – 13 – 606212 – 1/ -13:  978 – 0 – 13 – 606212 – 7

Reference textbooks: (Should be found in School Library)

Discrete – Event System Simulation, 5^th, Editions 2010, Jerry Banks, John S.     Carson II, Barry L. Nelson, and David M. Nicol), Pearson, 

ISBN-10: 0 – 13 – 606212 – 1/ -13:  978 – 0 – 13 – 606212 – 7

Additional Reading Sources and software: (database available through library)

Simulation Society (INFORMS – SIM)

Society for Modeling and Simulation International (SCS)

www.informs-cs.org/wscpapers.html

Course requirements   

All course materials are posted on the Brightspace course web page. Students are expected to read lecture materials before class,

· Class attendance is mandatory.

· Students must submit their HW before the beginning of each class.

· HW will not be graded, It will be discussed in class

· Exams will consider all materials covered in lectures, which may not be in the textbook. 

Policy

All participants are expected to always handle themselves with professional conduct. Students are expected to adhere to all university policies and uphold academic integrity throughout the course.

The Department of Technology Management and Innovation does not permit remote attendance in any of its fully on-campus course sections.

If you encounter a situation that will prevent you from attending your classes in person for more than one session, you should reach out to your Academic Advisor as soon as possible to discuss the available options (Term Withdraw, Leave of Absence, etc.). If you are sick and unable to attend a single session, you should contact your classmates for any notes or materials that you may have missed. If you require an excused absence to make up an assignment, please contact the Office of Student Advocacy <[email protected]> to apply for one.

Please note that if it comes to the attention of the department that you have not been attending your classes, but have been submitting work remotely, you will be subject to total withdrawal from these classes with potential full tuition and fee liability.

Grading

· Homework, Discussion, and participation, [15%]

· Attendance, [10%]

· Term Problem [12/10/2025], [15%]

· Midterm Exam [10/22/2025], [30%]

· Final Exam [12/17/2025], [30%] 

Grading Ranges:

Your final grade in the clas05s will be determined based on the summation of the number of points you acquire.  The following point spread corresponds with the next grade.

Part I: [Introduction to Discrete – Event System Simulation]

[09/03/2025] Lecture 1 “Introduction to System Analysis and simulation.” 

· overview of computer simulation,” will supply answers to the following fundamental questions in a computer simulation:

· What is the system?

· What is a discrete-event simulation? What is a continuous simulation? What is a Monte Carlo simulation?

· What are simulation experimentation and optimization?

· basics of discrete-event system modeling & simulation,” aims to provide a step–by–step procedure for performing a discrete event simulation.

· understand the fundamentals of DES modeling.

· Reading Chapters 1

o pp. 4 to pp.21

[09/10/2025] Lecture 2 “Simulation Example in Spreadsheet.”

· to introduce and illustrate the principles of simulation using spreadsheet examples.

· to know what modeling components and a reference are model.

· to know what formal model is and how it is specified?

· To know how to describe

· to know what the integrated framework of discrete-event system modeling is?

· Reading Chapter 2

o pp. 25 to pp.77 

II Mathematical and Statistical Models 

[09/17/2025] Lecture 3 “Review of Basic Probability and Statistics.”

· to review basic probability and statistics and introduce terminology and concepts related to simulation.

· to discuss a typical application of statistical models or distribution forms for both discrete and continuous distributions

· to define the errors and potential dangers of applying classical statistical techniques based on independent observations of simulation output data.

· Reading Chapter 5

o pp. 171 – pp. 219 

[09/24/2025] Lecture 4 “Queueing Models.”

· to discuss the general characteristics of queues models

· discuss meaning and relationships of the critical performance measures.

· learn how to estimate mean measures of performance from a simulation.

· be able to know the effect of varying the input parameters.

· be able to learn the mathematical solutions of a small number of essential and fundamental queueing models.

· Reading Chapter 6

o pp. 228 – pp. 268

Part III: [Random Numbers]

[10/01/2025] Lecture 5 “Random Number Generators”

· to learn the essential ingredient needed for every method of generating random variate.

· to learn how to sample and illustrate some widely used techniques for generating random variates from discrete as well continuous distributions.

· Reading Chapter 7

o pp. 277 - 294 

[10/08/2025] Lecture 6 “Random-Variate Generator.”

· to develop an understanding of generating samples from a specified distribution as input to a simulation model

· Reading Chapter 8

o pp. 300 – 327 

Part IV: [Analysis of Simulation Data] 

[10/15/2025] Lecture 7 “Input Modeling.”

· to draw the statistical aspects of fitting probability distributions to data

· to discuss and learn the steps of input model development.

· to identify a probability distribution to represent the input process and choose parameters for the named distribution.

· to evaluate the chosen distribution and parameters for the goodness of fit

· Reading Chapter 9

o pp. 335 – 369 

[10/22/2025] Lecture 8 “Mid - Term Exam.”

[10/29/2025] Session 9 “Verification, Calibration, and Validation of Simulation Models.”

· to develop and produce a model that represents actual behavior. closely enough for decision-making purposes.

· to illustrate methods and techniques of increasing model’s credibility to an acceptable level

· to define steps of building models by validations, verifications, and calibration

· Reading Chapter 10

o pp. 388 – 414

 [11/05/2025] Session 10 “Estimation of Absolute Performance I”

· to understand how to estimate absolute performance and its precisions.

· to define and understand the purpose of the statistical analysis and be able to determine the standards error and the width of a confidence interval.

· to figure out the number of observations required to achieve a standard error or CI of a given size.

· Reading Chapter 11

o pp. 417 – 440

o pp. 444 – 449

 [11/12/2025] Session 11 “Estimation of Absolute Performance II”

· to figure out the number of observations required to achieve a standard error or CI of a given size.

· Reading Chapter 11

o pp. 417 – 440

o pp. 444 – 449

 [11/19/2025] Session 12 “Estimation of Relative Performance.”

· to define methods for Comparison of alternative system designs

· to apply statistical methods to compare two or more system designs and define whether observed differences are due to differences in design or due to the random fluctuation inherent in the models.

· to define ways of two or more system comparisons

· Reading Chapter 12

o pp. 463 – 499

· 

[11/26/2025]  No Classes Friday Schedule

[12/03/2025] Lecture 13 “Simulation of Manufacturing and Material – Handling System.”

[12/10/2025] Lecture 14 “Case Problem Presentations and Course Review.”

[12/17/2025] Lecture 15 “Final Exam.”