Coursebooks

Optimization and simulation

MATH-600

Lecturer(s) :

Bierlaire Michel

Language:

English

Frequency

Every year

Remark

Every year/ Next time: Spring 2021

Summary

Master state-of-the art methods in optimization with heuristics and simulation. Work involves: - reading the material beforehand - class hours to discuss the material and solve problems - homework

Content

Part 1: Simulation

Sheldon M. Ross (1997) Simulation

Draws (Chapters 4 & 5)

Discrete event simulation (Chapter 6)

Statistical data analysis, bootstrapping (Chapter 7) 

Variance reduction techniques (Chapter 8) 

Markov Chain Monte Carlo methods (Chapter 10)

 

Part 2: Optimization:

heuristics Bierlaire M. (2015) Optimization: principle and algorithms  Classical optimization problems (chapter 25) 

Greedy heuristics (section 27.1) 

Neighborhood and local search (section 27.2) 

Diversification (sections 27.3 and 27.4)

Keywords

optimization, simulation

Learning Prerequisites

Required courses

 

Analysis, algebra, probability and statistics, Python programming language

Supervision

Office hours	Yes
Assistants	Yes
Forum	Yes

Resources

Bibliography

Bierlaire M. (2015) Optimization: principles and algorithms, EPFL Press

Ross S. (2013) Simulation, Elsevier

Ressources en bibliothèque

• Simulation / Ross S.
• Optimization : principles and algorithms / Bierlaire M.

Websites

• http://transp-or.epfl.ch/

Moodle Link

• http://moodle.epfl.ch/course/view.php?id=6791