First page
NEWS!!!!
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The take home exam is now available
Assignment 2 is now available below.
The consultation time on Thursday May 3 will be between 13-14.00.
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The course consists of two parts:
- discrete event simulation
- heuristic methods (with optimization)
Teachers and administrators
Björn Landfeldt, Bjorn.landfeldt@eit.lth.se (course coordinator, teacher)
MohammadHassan Safavi, mohammadhassan.safavi@eit.lth.se (teacher, heuristics)
Anne Andersson, anne.andersson@eit.lth.se (course administrator)
Lectures and exercise classes
Day and time |
Type |
Contents |
Tue 20 March, 13-15 |
Lecture S1 |
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Lecture S2 |
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Tue 27 March, 13-15 |
Lecture S3 |
random number generation, validation and verification, error propagation |
Wed-Thu 28-29 March, 10-12, 8-10 (Thu) |
Lab Exercise 1 |
discrete event simulation (problems to be solved during exercise) Event scheduling (program for exercise and home assignment) Process interaction (program for exercise and home assignment) |
Tue 17 April 13-15 |
Lecture S4 |
|
Thu 19 April 8-10 |
Lecture S5/O1 |
Large scale simulations, scripting and state machines, dynamic simulations. introduction to optimization, linear programming, duality, simplex method, sensitivity |
Tue 24 April 13-15 |
Lecture O2 |
Mixed integer programming, branch and bound method, examples |
Thu-Fri 26-27 April 8-10, 10-12 (27/4) |
Lab Exercise 2 |
solving optimization problems in MATLAB A brief introduction to MATLAB solvers for Linear and Integer programs Example 1: linear program (MATLAB file) Example 2: integer program (MATLAB file) Example 3: linear program and sensitivity analysis (Excel file) Lab Exercise: Solving Linear and Integer Programs using MATLAB (download pdf) |
Wed 2 May 10-12 |
Lecture H1 |
Introduction to heuristic methods, Local Search (LS) (lecture notes) |
Tue 8 May 13-15 |
Lecture H2 |
Simulated Annealing (SA), Tabu Search (TS), and Iterated Local Search (ILS) (lecture notes) |
Tue 15 May 13-15 |
Lecture H3 |
Genetic Algorithms (GA) (lecture notes) |
Thu 17 May 8-10, 10-12, 13-15 (11/5) |
Lab Exercise 3 |
Heuristic methods: simulated annealing, genetic algorithms, and Tabu search Lab Exercise: Metaheuristic simulations (code and instructions) |
The times and places can be found by using LTH’s schedule generator.
Take Home assignments
The assignments can be done in groups of two students or individually.
If you need help with assignments, you can come to Björn's office (room E:3145) at the following times:
- Wednesday 2 May 13.00-15.00
- Thursday 3 May 13.00-15.00
- Thursday 22 May 13.00-15.00
- Thursday 24 May 8.00-10.00
There are two take-home assignments that you must complete to pass the course. There will be one on basic discrete event simulation and one on large-scale and dynamic simulations. The deadlines for the assignments can be found below. If a take-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 such deadline, you cannot get a grade higher than 3.
The deadlines for the take-home assignments:
- Discrete Event Simulation foundations: 6 May
- Large scale simulations:3 June
The take-home assignments:
Material for take home assignment 1
For take-home assignment 1 you can use a matlab program that calculates confidence intervals by estimating the correlation between samples:
Link to a program for calculating the confidence intervals of your results.
Link to a description to the output of the program above.
However, writing the code yourself, e.g. in Matlab is not difficult and a good exercise (perhaps easier than using the file above).
If you want to write to a file that can be used as input to matlab, you can do it as in this example. The example is almost the same as "Event scheduling approach" above. One class is added (SimpleFileWriter). An instance is created and used in State, and the file is closed in Template2006.
Laboratory Exercises
There are three labs in the course. Lab 1 is voluntary and aimed at getting started with programming. Labs 2 and 3 are mandatory and must be passed in order to pass the course.
Take-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 labs |
Home exam |
Final grade |
Passed |
Not done or not passed |
3 |
Passed within deadline |
Passed |
4 |
Passed within deadline |
Passed with distinction |
5 |
Literature
For the discrete event simulation part of the course the following books can be used:
- C.A. Chung, Simulation Modeling Handbook – A Practical Approach, CRC Press (the book can be found as a free e-book in the univeristy library, see http://www.crcnetbase.com/ISBN/9780203496466 should be free for computers connected to the university's network)
- H. Perros, Computer Simulation Techniques: The Difinitive Introduction, available online.
Another good book that deals much more with the statistical side of discrete event simulation is:
- S. M. Ross, Simulation 4th ed., Academic Press 2006
For more exensive analysis of time series from a statistical POV.
- Andreas Jakobsson, An Introduction to Time Series Modeling 2nd ed., Studentlitteratur 2015
An excellent free textbook on convex optimization (including linear) is available for free here.
Matlab has a good introduction to working with optimisation in their environment. This is useful for the optimisation lab.
Here is a link to a list of error propagation rules using logarithmic transformation