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Syllabus for

Academic year
EEN025 - Constraint programming and applied optimization  
Villkorsprogrammering och tillämpad optimering
 
Syllabus adopted 2018-02-14 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering
Department: 32 - ELECTRICAL ENGINEERING


Teaching language: English
Open for exchange students: Yes
Block schedule: B

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0118 Examination 5,0c Grading: TH   5,0c   29 Oct 2018 am H   08 Jan 2019 am M   27 Aug 2019 pm M  
0218 Laboratory 2,5c Grading: UG   2,5c    

In programs

MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)

Examiner:

Martin Fabian


  Go to Course Homepage

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

Discrete Event Systems (or comparable knowledge acquired by other means).

Aim

The course aims to give advanced knowledge about formal methods in general and constraint programming in particular, and how these methods may be used for reasoning about and analyzing the performance of discrete event systems. Typical applications for using formal methods are verification and synthesis of control functions for embedded systems, control and optimization of automated production systems, and communication systems. The course focuses on modeling, specification, simulation, verification, synthesis, and optimization.

Learning outcomes (after completion of the course the student should be able to)

Understand the two mainstream paradigms of Mixed Integer Linear Programming (MILP), and Constraint Programming (CP).

Explain the differences between MILP and CP, and be able to convert models from one paradigm to the other.

Analyze and decide which paradigm is probably the better choice given different situations.

Comfortably model optimization problems using either of the approaches.

Use state-of-the-art modeling languages (AMPL - MiniZink) and general-purpose solvers (CPLEX, Gurobi, GECODE)

Understand and implement special-purpose heuristic algorithms such as A* and Dijkstra¿s.

Content

The main objective of this course is to give the students the ability to understand and confidently use the optimization tools and techniques from Operations Research (OR) and Computer Science (CS) area, through hands-on tasks. This includes theory and practice of general optimization techniques such as mixed integer-linear programming, constraint programming, branch and bound and other discrete optimization algorithms (Dijkstra's, A*).

Organisation

The course comprises lectures, exercises, and a number of assignments that address important parts of the course. These hand-in assignments involve modeling, specification, verification, synthesis, and optimization. The hand-in assignments may be peer-reviewed in a scientific conference type of setting.

Literature

Martin Fabian, Lecture notes. Additionally scientific papers and other extra material may be handed out.

Examination including compulsory elements

Examination is based on a conventional written exam, as well as passed hand-ins.


Published: Mon 28 Nov 2016.