Första sida
Teachers and administrators
Michal Pioro, Michal.Pioro@eit.lth.se, (the white hours in the table below)
Saeed Bastani, Saeed.Bastani@eit.lth.se (the blue hours in the table below)
Christian Nyberg, Christian.Nyberg@eit.lth.se, (course coordinator)
Marianne Greiff-Svensson, marianne.greiff_svensson@eit.lth.se, (course administrator)
Lectures and exercise classes
Date and time |
Type |
Contents |
19 March, 8-10 |
Introduction to simulation, notions and notations, examples |
|
20 March, 8-10 |
Event scheduling and process interaction approach |
|
26 March, 8-10 |
Lecture |
Random number generation, choosing input distribution |
27 March, 8-10 |
Lecture |
Confidence analysis of output data |
31 March, 13-15 |
Introduction to optimization, basic notions including convexity and duality, examples including multi-commodity flow problems |
|
2 April, 8-10 |
Linear programming, Simplex I |
|
3 April, 8-10 |
Exercise |
Solving optimization problems in class |
4 April, 13-15 |
Simplex method II |
|
7 April, 8-10 |
Computer exercise: introduction to optimization tool (Gurobi) |
|
8 April, 8-10 |
Duality in linear programming, column generation through dual separation. |
|
8 April, 13-15 |
Computer exercise: solving linear programs |
|
9 April, 8-10 |
Lecture |
Simulating process trajectories, what it really means |
10 April, 8-10 |
Lecture |
Verification and validation, variance reduction |
5 May, 8-10 |
Integer programming, branch and bound. |
|
6 May, 8-10 |
Exercise |
Solving optimization problems in class |
7 May, 8-10 |
Modeling non-linearity, notion of computational complexity including NP-hardness. |
|
7 May, 13-15 |
Exercise |
Solving optimization problems in class |
8 May, 8-10 |
Exercise |
Computer exercise: solving integer programs |
12 May, 8-10 |
Lecture |
Monte Carlo techniques |
14 May, 15-17 |
Question |
Opportunity to ask questions on discrete event simulation |
15 May, 8-10 |
Question |
Opportunity to ask questions on discrete event simulation |
20 May, 8-10 |
Heuristic methods: optimization through simulation, local search and randomness: simulated annealing |
|
20 May, 10-12 |
Evolutionary algorithms, GRASP |
|
21 May, 8-10 |
Exercise |
Optimization and heuristics – solving problems in class |
21 May, 13-15 |
Exercise |
Computer exercise: solving combinatorial optimization problems using heuristic methods |
22 May, 8-10 |
Exercise |
Computer exercise: solving combinatorial optimization problems using heuristic methods |
The times and places can be found by using LTH’s schedule generator.
There will also be eight consulting hours for the two home assignments on discrete event simulation. The consulting hours are:
Home assignments
There are five home assignments that you must complete to pass the course. There will be two on discrete event simulation, two on optimization and one on heuristic methods. The deadlines for the home assignments can be found below. If a home assignment is not approved in the due time, you will get a second chance to hand it in, but not later than one week after when you got it back. If you miss any of such deadlines, you cannot get a grade higher than 3.
You can do the home assignments in groups of not more than two people. If there are two students in the group, the teachers will have a short discussion with the group to secure that both have contributed to the solutions.
Here are the home assignments:
Assignment 1 Template java program for event scheduling which can be used in assignment 1
A matlab program for calculating the confidence intervals
A description of the output of the matlab program above
Assignment 2 Template program for process interaction method for assignment 2
Home exam
To get a higher grade than 3, you must pass the take-home exam. From the take-home exam you can get the grades not passed, passed and passed with distinction. Observe that the take-home exam is to be done individually, that is, you may not do it in cooperation with other students.
Final grade
Your final grade will be determined by the table below.
Home assignments and project |
Home exam |
Final grade |
Passed |
Not done or not passed |
3 |
Passed within deadline |
Passed |
4 |
Passed within deadline |
Passed with distinction |
5 |
Literature
The following literature will be used in the optimization part and in the heuristic methods part of the course:
- M. Pióro and D. Medhi. Routing, Flow, and Capacity Design in Communication and Computer Networks, Morgan-Kaufmann, 2004
- L. Lasdon. Optimization Theory for Large Systems, MacMillan, 1970
- L.A. Wolsey. Integer Programming, J.Wiley, 1998
- M. Pióro. Network Optimization Techniques, Chapter 18 in E. Serpedin, T. Chen, and D. Rajan: Signal Processing, Communications, and Networking, CRC Press, 2012
- M. Minoux, Mathematical Programming, Theory and Algorithms, J. Wiley and Sons, 1986
For the discrete event simulation part of the course the following book can be used:
- C.A. Chung, Simulation Modeling Handbook – A Practical Approach, CRC Press
Deadlines
Here are the deadlines for the home assignments:
Assignment |
Subject of assignment |
Deadline |
1 |
Event scheduling approach |
19 May |
2 |
Process interaction approach |
19 May |
3 |
Optimization |
19 May |
4 |
Optimization |
2 June |
5 |
Heuristic methods |
2 June |