Weeks |
Topics |
1-3 |
MODULE 1: BACKGROUND
-1.1 Introduction, Motivation for Simulation, Overview of course
-1.2 Probability Review (Bernoulli, Binomial, Uniform, Triangular, Exponential, Geometric distributions)
-1.3 Normal + Lognorrmal, Central Limit Theorem (CLT), Sample vs. Population Statistics, Goodness of Fit
======SUGGESTED READING=======
-Probability Overview
Readings:[Ross: Chapter 1 ]
-Matlab Overview
If needed you can try the self-paced Matlab tutorial "Onramp", see https://www.mathworks.com/services/training.html?s_tid=gn_trg_ov
Matrices and Arrays, Graphics]
-Probability Review
[Ross: Chapter 2 (2.1-2.6 (skim 2.6))]
-Bernoulli & Binomial Distributions
[Ross: Chapter 2 (2.8, pp. 18-19)]
-Uniform, Triangular Distributions [Ross: Chapter 2 (2.9, p.23-24)
-Exponential, and Geometric Distributions [Ross: Chapter 2 (2.8, pp. 21-22, 2.9, pp. 26-28)]
Normal Distribution, Central Limit Theorem
[Ross:Chapter 2 (2.9, pp. 24- 26)]
-Sample vs. Population Statistics
[Ross: Chapter 38, 8.1-8.2, pp.135-144 (skim)]
-Goodness of fit
[Ross: Chapter 11, 11.1-11.2, pp.257].
|
4-7 |
MODULE 2: SIMULATION METHODOLOGY/BAYES THEOREM
-2.1 Pseudo-Random Number Generation
-2.2 Generating Random Variables: Discrete, Inverse Transformation Method (ITM)
-2.3 Generating Random Variables: Continous, Inverse Transformation Method (ITM)
-2.4 Generating Random Variables: Acceptance-Rejection Method (ARM)
-2.5 Convolution Method for Normal Distribution Based on the CLT
-2.6 Poisson Distribution, Bayes Theorem
-2.7 Variance-Reduction Techniques (antithetic variables, control variates)
======SUGGESTED READING=======
-Pseudorandom Number Generation
[Ross: Chapter 3]
-Generating Random Variables [Ross: Chapters 4 and 5
4.1-4.3 for ITM, 4.4 for ARM, discrete random variables
5.1-for ITEM, 5.2 for ARM
]
-Normal Distribution, Central Limit Theorem
[Ross:Chapter 2 (2.9, pp. 24- 26)]
-Poisson Distribution
[Ross:Chapter 2 (2.8, pp. 19- 21)]
-Bayes Theorem
[material provided in the lectures]
-Variance-Reduction Techniques
[Ross:Chapter 9 (9.1- antithetic variables , pp.155-159
9.2 control variates
pp. 162-169.]
|
8 |
SPRING BREAK- NO CLASS |
9 |
EXAM ON MODULES 1 & 2
|
10-11
|
MODULE 3: SIMULATION PROJECT #1, WIND POWER & DEMAND RESPONSE, & PROJECT #2, MANUFACTURING
-3.1 Wind Power Background, Demand Response
-3.2 Wind Power Project Overview
-3.3. Manufacturing Case Study Overview
======SUGGESTED READING=======
-Will be provided in the lecture materials |
12 |
MODULE 3: SIMULATION PROJECT #1, WIND POWER & DEMAND RESPONSE, & PROJECT #2, MANUFACTURING
-Presentation of project results
======SUGGESTED READING=======
-Will be provided in the lecture materials |
| 13-15 |
MODULE 4: STOCHASTIC DYNAMIC PROGRAMMING
-4.1 Background on Deterministic Dynamic Programming
-4.2 Background on Stochastic Dynamic Programming
-4.3 Connection Between SDP and Real Options
-4.4 Discussion of assignment on stochastic dynamic programming
======SUGGESTED READING=======
-Will be provided in the lecture materials
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