INDE 504 Discrete-Event Simulation – Fall 2023

Instructor: Bacel Maddah

Syllabus

Class Notes (over a tentative schedule)

Week 1   

OR and Simulation Modeling

Excel simulation for the game of Craps

Weeks 2-4

Probability and Random Variable Review

Introduction to Python

Weeks 5-6

Queueing Primer

Introduction to Discrete Event and Process Oriented Simulation

G/G/1 Hand Simulation

Simulation Verification and Validation

Weeks 7-8

Input Analysis I:  Overview, data collection, hypothesizing distribution, parameter estimation –

VIDEOS

Distribution fitting with Scipy

Weight-height data set 

Introduction to SimPy

Week 9-10

Input Analysis II:  Goodness of fit, what to do with no data

Random Number Generators (1): Overview, Linear Congruential Generators, Good and Common LCGs

Chi Square Test on Drive-through Inter-arrival data 

Weeks 11 – 12

Random Number Generators (2):  Testing RNGs, Empirical Tests, Chi^2, KS, Runs

Generating random Varietes (1):  Inverse Transform, Composition, Convolution

Generating random Varietes (2):  Acceptance-Rejection, Uniform, Triangular, Normal, Lognormal,  Bernoulli, Binomial, and Geometric Distributions

Simulation with Simpy

Inverse Transform dot Excel 

Excel model for generating Z based on 12 Us

Week 13

Generating random Varietes (3):  Empirical Distributions,  Stationary Poisson Process, Nonstationary Poisson

Output Analysis 1:  Nature of Output, Transient and Steady States, Terminating / Nonterminating   Simulations, Estimating Means

Output Analysis 2:  Choosing Initial Conditions, Steady State Analysis,  Replication/Deletion, Batch Means, Comparing Two Configurations

Output Analysis 3:  Comparing > 2 Systems, Comparing with Standard, Variance Reduction, CRN, Antithetic Variates, Control Variates

Excel simulation of a Poisson Process

Week 14

Project Presentation on Thursday Nov 30 from 9:30 AM to 1:00 PM.  

Project template (to be followed strictly on font size and type, line spacing, margins, page limit, etc.)

Presentation tips

 Homework Assignments

HW 1a    Python intro;  Excel file.  Due Mon, Sep 18.  Please upload the files to Moodle. 

HW1b    Probability refresh.  Due Tuesday, Sep 26.  In hard copy, at the beginning of the class. 

HW 2  KSZ book: 3-4, 3-6, 3-7, 3-9, 3-13, and supplement problems.    Due Monday, November 6.  Submit on Moodle.   

HW 3  BCNN book, Ch 9 (pp. 376-379): 9.7, 9.11, 9.12, 9.14, 9.17, 9.19,  KSZ book: 4-1, 4-3, 4-9, and distribution fitting with Scipy; Housefly wings’ length data set.  Due Wednesday, November 15.  Submit on Moodle.  

HW 4. BCNN book, Ch 7 (p. 293): 7.2, 7.4, 7.5, 7.6, 7.16 (use X_{j+1} = (X_{1,j} – X_{2,j} + X_{3,j})mod 32,362), Ch 8 (pp. 326-330): 8.4, 8.5, 8.9, 8.10, 8.14, 8.29 (you may use Excel RNG) and Supplement Problems.   Due Tuesday, November 28.  Submit on Moodle.

HW 5.  BCNN book, 11.1, 11.4, and Supplement Problems.  Due with final exam. 

Links

Taleb delivers AUB’s commencement speech

Flaw of averages video

Winter Simulation Conference archive