Masood Fathi
Associate Professor of Production Engineering
School of Engineering Science
Engineering Optimization: Methods and Applications A1F
Course, Master's (2nd cycle),
6 credits,
VP747A
Application
Choose a course instance to see course syllabus and admission requirements.
Spring 2024, Location: Skövde, Pace of study: 40%
Application
Application is done after nomination.
Course syllabus, with reading list
When? Where? How?
Study period: 25 March 2024 - 2 June 2024
Location: Skövde, Campus, Daytime
Pace of study: 40%
Admission Requirements
A Bachelor’s degree equivalent to a Swedish kandidatexamen of 180 credits, within the main fields of integrated product development, production engineering, automation engineering, mechanical engineering or information technology (or the equivalent) and passed course VP702A Computational Intelligence A1N (or the equivalent).
A further requirement is proof of skills in English equivalent of studies at upper secondary level in Sweden, known as the Swedish course English 6. This is normally demonstrated by means of an internationally recognized language test, e.g. IELTS or TOEFL.
Selection
Guaranteed admission.
Language
The courses are conducted in English.
This course aims to provide students with knowledge about the exact and approximation solution approaches for solving a variety of engineering optimization problems. The course covers both optimization methods and their applications for solving real-life problems. The primary focus of the course lies on learning (non-) linear mathematical models including formulating, solving and analyzing of such models. The secondary focus lies on learning exact and (meta-) heuristic algorithms. In this course, the students will also learn to differentiate optimization problems mainly concerning possible solution approaches and apply appropriate solutions to each specific problem.
The course contains both a theoretical and a practical part. The theoretical part focuses on the development of the mathematical models and their applications as well as learning different optimization methods and algorithms. The practical part includes computer labs to learn relevant computer software to solve mathematical models as well as to implement, test and apply the learned optimization methods and algorithms for solving different types of industrial and non-industrial problems.
