Multi-Criteria Optimization and Decision Analysis for Embedded Systems Design

Module Number: EI71035

Duration: 1 Semester

Ocurrence: Winter Semester

Language: English

Number of ECTS: 5

Staff

Professor in charge: Dr. Anh Vu Doan

Amount of work

Contact hours: 60

Self-study hours: 90

Total: 150

Description of achievement and assessment methods

Written examination (75 minutes): The students will be examined through a written examination where they prove that they have understood the application of the multi-criteria paradigm and can apply it to perform analysis, modeling, optimization, and decision making for problems encountered in embedded systems design. The questions will cover the theoretical background presented during the lectures as well as exercises from the lecture and the lab. The examination lasts 75 minutes and will be carried out without helping material.

Laboratory: The work in groups of 2-3 participants will be assessed, where they have to demonstrate that they can solve real world optimization problems coming from a current research area. Given the size of such problems this cannot be covered in the written exam. The assessment process of this part will be carried out through deliverables and a subsequent discussion. The final grade is the weighted average of the written examination (60%) and the lab part (40%).

(Recommended) requirements

- Data structures

- Basic programming skills in Python or Matlab; alternatively C/C++ or Java

- Basic knowledge of probability and statistics (probability axioms and theorems, e.g. Bayes' Theorem and its applications; typical probability distributions, e.g. exponential, Gaussian, etc.)

Contents

Content of the lecture 1. Introduction to the multi-criteria paradigm for embedded systems design - Uni-criterion vs multi-criteria - Modeling and challenges 2. Optimization methods - Linear programming - Metaheuristics (e.g. genetic algorithms, simulated annealing) - Multi-objective optimization for design space exploration

3. Decision making processes - Voting theory - Multi-criteria decision analysis - Game theory - Decision under risk and uncertainty Content of the laboratory The lecture content is applied in the accompanying laboratory focusing on solving real-world design problems of embedded systems. The students will have to perform the following tasks: problem abstraction and modeling, algorithm selection and implementation, multi-criteria decision making and analysis. Thereby both functional and nonfunctional aspects will be considered. The students will be guided in subsequent steps through the optimization process.

Study goals

Upon successful completion of this module, students are able to: - understand the multi-criteria paradigm and its challenges for embedded systems design, - analyze and model encountered problems with this paradigm, - understand how different (multi-objective) optimization methods work, select and apply the most suitable one(s) depending on the situation, - understand how different (multi-criteria) decision making methods work, select and apply the most suitable one(s), as well as analyze the results obtained after the optimization process.

Teaching and learning methods

The technical content will be introduced by means of lectures with PowerPoint presentations and will be illustrated with small examples that will be included in the slides. The students are encouraged to ask questions. In addition to the individual learning methods of the students, the transfer of the theoretical knowledge to its practical application will be achieved through exercises within the laboratory part. They will be carried out on computers (under the guidance of the lecturer) and deepen the lectures content. All the course material will be made available to the students through Moodle.

Media formats

The following media forms are used: - Presentations with handwritten annotations - Course material including lab manual with description of exercises

Literature

Optional literature recommendations: - XS. Yangi, "Engineering Optimization: An Introduction with Metaheuristic Applications",

Wiley 2010 - EG. Talbi, "Metaheuristics: From Design to Implementation",

Wiley 2009 - S. Greco, M. Ehrgott, J.R. Figueira (Eds.),

"Multiple Criteria Decision Analysis: State of the Art Surveys", Springer 2016