SCHOOL

ENGINEERING

ACADEMIC UNIT

ELECTRICAL & COMPUTER ENGINEERING

LEVEL OF STUDIES

Undergraduate

COURSE CODE

ECE_ΓK806

SEMESTER

8^th  

COURSE TITLE

LINEAR & COMBINATORIAL OPTIMIZATION

INDEPENDENT TEACHING ACTIVITIES
if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

WEEKLY TEACHING HOURS

CREDITS

Lectures

4hrs/wk

5

Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

COURSE TYPE

general background,
special background, specialised general knowledge, skills development

Specialised general knowledge 

PREREQUISITE COURSES:

Linear Algebra (ECE_Y104)

Algorithms and Data Structures (ECE_Y608)

LANGUAGE OF INSTRUCTION and EXAMINATIONS:

Greek

IS THE COURSE OFFERED TO ERASMUS STUDENTS

COURSE WEBSITE (URL)

https://eclass.upatras.gr/courses/EE916/

Learning outcomes

The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

Consult Appendix A

·    Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area

·    Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B

·    Guidelines for writing Learning Outcomes

After a successful completion of the course each student will be expected to know the basic mathematical theory required for linear constrained optimization, as well as the logic and steps of Simplex Algorithm for linear programming. To understand duality theory for linear programming and apply it whenever required for sensitivity analysis.  To be able to use specialized software for the solution of sufficiently large linear and integer programming problems. Lastly, for real-world problems to use special modelling techniques in order to construct models solvable by known solvers.

 General Competences

Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Adapting to new situations

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

Project planning and management

Respect for difference and multiculturalism

Respect for the natural environment

Showing social, professional and ethical responsibility and sensitivity to gender issues

Criticism and self-criticism

Production of free, creative and inductive thinking

……

Others…

…….

The course aims at developing skills mainly for independent work and occasionally for team and collaborative work, for effective decision making, as well as for promoting free, creative and inductive thinking that is essential for producing new research ideas.

Modelling optimization problems using linear programming. Simplex algorithm. Duality theory for Linear Programming. Complementary slackness. Dual – Primal Simplex algorithms. Sensitivity analysis. Integer programming. Branch-and-bound method. The knapsack problem. Travelling salesman problem. Quadratic programming. Modelling techniques with the help of integer variables. Network Simplex algorithm. Transportation and transhipment problem. Network Flow problems. Interior point methods.

DELIVERY
Face-to-face, Distance learning, etc.

Face to Face in classroom

USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
Use of ICT in teaching, laboratory education, communication with students

Lectures are performed with the help of viewgraphs projected in class simultaneously with the use of whiteboard for better understanding of the analytical techniques needed in theory and exercises. In addition, the electronic platform “eclass” of the University is used for better support of students’ educational needs and also for communicating with them. The   

TEACHING METHODS

The manner and methods of teaching are described in detail.

Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

STUDENT PERFORMANCE EVALUATION

Description of the evaluation procedure

Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

The final grade for the course results from each student’s performance on:

¬  the 3 homework sets (35% of the grade)

¬  the final exam or project (65% of the grade)

- Suggested bibliography:

- Related academic journals: