Optimization and simulation
MATH-600 / 4 credits
Teacher: Bierlaire Michel
Language: English
Remark: Every year/ Next time: Spring 2021
Frequency
Every year
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
Websites
Moodle Link
In the programs
- Exam form: Multiple (session free)
- Subject examined: Optimization and simulation
- Lecture: 35 Hour(s)
- Practical work: 55 Hour(s)
- Exam form: Multiple (session free)
- Subject examined: Optimization and simulation
- Lecture: 35 Hour(s)
- Practical work: 55 Hour(s)
- Exam form: Multiple (session free)
- Subject examined: Optimization and simulation
- Lecture: 35 Hour(s)
- Practical work: 55 Hour(s)
Reference week
| Mo | Tu | We | Th | Fr | |
| 8-9 | |||||
| 9-10 | |||||
| 10-11 | |||||
| 11-12 | |||||
| 12-13 | |||||
| 13-14 | |||||
| 14-15 | |||||
| 15-16 | |||||
| 16-17 | |||||
| 17-18 | |||||
| 18-19 | |||||
| 19-20 | |||||
| 20-21 | |||||
| 21-22 |
Légendes:
Lecture
Exercise, TP
Project, other