Learning outcomes

After this course, the student must be able to

  • model an optimisation problem ;
  • write a linear programming problem ;
  • graphically solve a simple linear programming problem;
  • use the simplex alogorithm to solve a linear programming problem;
  • analyse and comment on the solution of a linear programming problem.


The student will also be assessed on its understanding of the topics of this course (see content).  He must be able to explain the theory and the methods addressed during the lessons.  Also, he must be able to review a hot topic in operational research.

 

Content

This course aims to introduce the student to operational research.  Linear programming is seen in details, but other related topics are addressed, such as game theory, Markov decision processes, non-linear optimisation, etc.  Special attention will be paid to the correct (and suitable) use of models and theoretical concepts will be illustrated.

Assessment method

The course is divided in two activities.  First, the courses and the exercices are evaluated by an oral examen.  Second, a continous evaluation assesses the ability to review a hot topic in operation research and to make an oral presentation of the results.  The exam and the continuous evaluation amount for 80% and 20% of the final grade, respectively.

Language of instruction

Anglais
Training Study programme Block Credits Mandatory
Bachelor in Computer Science Standard 0 5
Bachelor in Computer Science Standard 2 5