Due Dates

Unless otherwise specified, Learning Group Assignments should be completed for the next lecture day.

Semester Calendar

,
Date Lecture Title Assignment(s) Lecture & Assignment Goals
Notes
Wed Dec 8

Classes end

Tue Dec 7 Quiz 3
Wed Nov 24 – Fri Nov 26

Thanksgiving Break

Fri Nov 19

Last day to withdraw for all students

Thu Nov 4 Quiz 2
Mon Oct 18 – Tue Oct 19

Fall Break

Thu Sep 30 Quiz 1
Thu Sep 23 Uniform Arrivals LG Assignment:

lga-uniform-arrivals


Individual Assignment:

War-and-Trash

Know that uniform arrival times and uniform interarrivals are not the same thing.

Understand the steps in going from uniform arrival times to exponential interarrival times.

Know the Exponential(mu) random variate and the interpretation of its parameter mu in the context of arrival times.

Know the F(x) inversion technique for constructing random variates. What is the requirement on F(x)?

Know the Geometric(p) random variate, it's parameter p, and it's connection to Exponential(mu)

Exponential and Geometric distributions and variates

Wed Sep 22

Career Day virtual (NO EXAMS)

Tue Sep 21 Random Points LG Assignment:

lga-random-points


Individual Assignment:

War-and-Trash

Know how to use accept/reject techniques for uniformly random (geometric) point generation.

Know the pitfalls associated with common (naive) methods of random point generation.

What unique characteristic of a system or simulation makes it Monte Carlo?

Know the tell-tale feature(s) of spatial plots produced by faulty point generation algorithms.

When randomizing points in a circle without accept/reject, how should the radi r be chosen using Random()?

Why is it important to use multiple seeds and many replications in Monte Carlo simulations.

Know the Equilikely(a,b) random variate: the meaning of its parameters, pmf, and CDF.

Know the F(x) inversion technique for constructing random variates. What is the requirement on F(x)?

Know the Uniform(a,b) random variate: the meaning of its parameters, pdf, and CDF.

Understand the problems with the often used and always flawed RandomInteger() mod SIZE programming pattern.

What does the parameter u in random variates represent? Computationally, how do we get a value for u in code?

Know the difference between a random number and a random variate.

Lecture slides for Monte Carlo simulations. Some of you may be missing §2.3 in your text, this might help.

Thu Sep 16 Monte Carlo Simulations LG Assignment:

lga-monte-carlo-probs


Individual Assignment:

Read about project requirements for the course; we'll review these (briefly) next lecture.

Know how to use accept/reject techniques for uniformly random (geometric) point generation.

Know how to write Monte Carlo simulations for estimating the Pr(A) of an event A.

What unique characteristic of a system or simulation makes it Monte Carlo?

When randomizing points in a circle without accept/reject, how should the radi r be chosen using Random()?

Why is it important to use multiple seeds and many replications in Monte Carlo simulations.

What are replications in the context of Monte Carlo simulations?

Understand the submission and I/O requirements of simuation projects for the course.

Know the relationships between PMF/PDF and CDF for both discrete and continuous distributions.

Know the Equilikely(a,b) random variate: the meaning of its parameters, pmf, and CDF.

Know the F(x) inversion technique for constructing random variates. What is the requirement on F(x)?

Know the Uniform(a,b) random variate: the meaning of its parameters, pdf, and CDF.

Understand the problems with the often used and always flawed RandomInteger() mod SIZE programming pattern.

What does the parameter u in random variates represent? Computationally, how do we get a value for u in code?

Know the difference between a random number and a random variate.

f(x), F(x) primer slides.

Lecture slides for Monte Carlo simulations. Some of you may be missing §2.3 in your text, this might help.

Tue Sep 14 No Lecture (In-person Career Day!)
Thu Sep 9 SIS wrap up & *Random()* thoughts LG Assignment:

No LGA, you can get ahead (probably a good idea for the next couple of lectures) by reading §2.3.

Know how to use accept/reject techniques for uniformly random (geometric) point generation.

What unique characteristic of a system or simulation makes it Monte Carlo?

When randomizing points in a circle without accept/reject, how should the radi r be chosen using Random()?

Why is it important to use multiple seeds and many replications in Monte Carlo simulations.

What does the pRNG API routine Random() provide to a simulation writer?

What is a seed for a pRNG, how is it related to the sequence of valued generated by Random()?

What is ρ (rho) for pRNGs? When does the sequence of values from Random() repeat?

Know the Equilikely(a,b) random variate: the meaning of its parameters, pmf, and CDF.

Know the F(x) inversion technique for constructing random variates. What is the requirement on F(x)?

Know the Uniform(a,b) random variate: the meaning of its parameters, pdf, and CDF.

Understand the problems with the often used and always flawed RandomInteger() mod SIZE programming pattern.

What does the parameter u in random variates represent? Computationally, how do we get a value for u in code?

Know the difference between a random number and a random variate.

chl_cycle-vs-random-results.pdf

pRNG primer slides

Lecture slides for Monte Carlo simulations. Some of you may be missing §2.3 in your text, this might help.

Wed Sep 8

Census Day

Tue Sep 7 Simple Inventory System (SIS) LG Assignment:

lga-coding-sis

Understand the Simple Inventory System (SIS), it's assumptions and simplifications.

Understand the experimental design of the Simple Inventory System case study; how was an optimal s determined?

How did back-ordering inventory manifest itself in the SiS conceptual, specification, and computational models?

How did flow-balanced inventory manifest itself in the SiS conceptual, specification, and computational models?

How did zero delivery lag manifest itself in the SiS conceptual, specification, and computational models?

In the SiS case study, what were s and S (note the case) and how did the simulation experiment vary one or both of them?

What SiS assumption about demand over an inventory review period simplified the specification model?

Considering q-bar in questions 1.2.2 and 1.2.8 group question.

Lecture slides for simple inventory systems.

Here is the Code base for LGA on SIS.

Mon Sep 6

Labor Day holiday - campus closed

Thu Sep 2 Coding SSQs LG Assignment:

lga-coding-ssqs

What is simulation validation?

What is simulation verification?

Name two acceptable ways to validate a simulation.

Know how the expected behavior or performance of an SSQ changes with varying levels of traffic intensity.

Know how to calculate traffic intensity and its connection to service rate.

Understand how a FIFO SSQ simulation can be written in a simple while loop and how ai and si can be manipulated for simple experiments.

Understand the canonical SSQ and appreciate its broad application to computer simulation.

Be familar with the job averaged statistics and time averaged statistics of an SSQ.

Know the algebraic form of Little's equations connecting the two types of statistics.

What properties must an SSQ have in order to apply Little's Equations to its statistical measures.

How were arrival times and service times "modeled" in the Sven & Larry case study?

Is the relationship between traffic intensity and average queue length linear or non-linear?

Know how to use an appropriate indicator function in the proof of Little's Theorem.

Know the "pattern of the proof" using indicator functions to show Little's equations for SSQs.

Which variable was altered Sven & Larry ice cream parlor simulation experiment? How was it altered? Was the measure of traffic intensity affected?

Lecture slides for Little's Equations and Traffic Intensity.

Here is the tarball with the provided ssq programs.

Considering V&V group question.

Tue Aug 31 Introduction to SSQs LG Assignment:

lga-intro-to-ssqs

Know the rules for presenting discrete and continuous data relationships (scatter plots vs connected dots).

What is simulation validation?

What is simulation verification?

Name two acceptable ways to validate a simulation.

Know how to calculate traffic intensity and its connection to service rate.

Understand the canonical SSQ and appreciate its broad application to computer simulation.

Be familar with the job averaged statistics and time averaged statistics of an SSQ.

Know the algebraic form of Little's equations connecting the two types of statistics.

Know the four different types of queuing disciplines that might be used in an SSQ simulation.

Which of the SSQ time measures (there are 6) are timestamps and which are time intervals?

LGA discussion slides

Simple Machine Shop slides

Considering V&V group question.

SSQ Slides

Tue Aug 31

First ACM Tech Talk; BB W250 6PM

Fri Aug 27

Graduate student registration deadline

Thu Aug 26 Simulation Design LG Assignment:

lga-engineering-a-simulation

Learn about several different simulation approaches, topics, and uses.

What are consistency checks? How can they be used in V&V?

What are the authors' five phases of simulation development?

What is simulation validation?

What is simulation verification?

What is the computational model of a simulation?

What is the conceptual model of a simulation?

What is the specification model of a simulation?

Name two acceptable ways to validate a simulation.

When can a particular phase (concept, specification, computational, v&v) of simulation development be skipped?

Intro to Simulation Design slides.

Some resources you'll need during lecture: show_article_discussion.pdf, new-learning-group-0-todo.txt.

Tue Aug 24 This is merely a simulation Individual Assignment:

Review the course syllabus and read how Learning Groups with participation points will be used in the course.

You may also want to look through the assignment submission guidelines so you know what you're getting into.

Intro slides

From this zip, read one paper (choose the one of most interest to you), and be prepared to discuss it in the next lecture.

Learn of several different simulations reported in the literature, compare and constrast their pros and cons.

Understand how the course participation grade will be factored into your course grade.

Understand how your course grade will be calculated.

You do not (yet) need a login for the course website, when the time comes you will authenticate using your mymail Mines Email address (through google).

This course will not use Canvas or Blackboard.

Mon Aug 23

Classes begin