Learning outcome

A candidate who has completed the course will have a learning outcome in the form of acquired knowledge, skills, and general competence, as described below.

Knowledge

The candidate:

• understands the fundaments of mathematical optimization
• recognizes and can classify different optimization problems
• understands the concept of local and global optimum
• understands the effect of different kind of constraints on the solution of optimization problems
• knows the most common numeric approaches to solve optimization problems, and their limitations
• understands the meaning of optimization solutions and their sensitivity to parameters variations.

Skills

The candidate:

• is able to recognize and formulate problems that can be solved using optimization
• is able to identify, classify and formulate optimization problems within the paradigms of Dynamic Programming (for sequential systems), Linear Programming, Quadratic Programming, Non-Linear Programming, and Mixed Integer Non-Linear Programming
• is able to identify and formulate problems using multi-objective optimization
• is able to choose and apply available solvers to find solutions to different optimization problems
• is able to apply different criteria to interpret and choose among multiple possible solutions of optimization problems
• is able to read and write scientific style documents describing optimization problems, solution algorithms and results interpretation.

General competence

The candidate:

• is able to analyze, formulate and solve overall engineering and industrial problems requiring different kinds of optimization, taking into account process, safety, economic, and environmental aspects.

Course Description

• Fundamentals of unconstrained mathematical optimization
• Optimization with equality constraints
• Optimization with equality and inequality constraints
• Convex optimization and global optima
• Discussion of common numeric approaches to optimization
• Discussion of common optimization paradigms: Dynamic Programming (for sequential systems), Linear Programming, Quadratic Programming, Non-Linear Programming, and Mixed Integer Non-Linear programming
• Introduction to evolutionary algorithms
• Introduction to multi-objective optimization
• Use of available optimization solvers
• Applications of optimization (data reconciliation, parameter estimation, model predictive control, process design, energy saving, and economic project evaluation among others).

Teaching and Learning Methods

Lectures, excercises at class and homeworks and an individual project.

An online, part-time version of the SCE study programme will start Fall 2015. The present course will be taught online from the fall semester year 2018. However, the course will continue to be taught also as a traditional campus-based course. The course contents and learning material used in the course will be the same in both programmes, except that in the online programme, the lectures will be in the form of offline video-based lectures, and laboratory assignments will be organized at a gathering on the campus at the end of the semester.

Assessment Methods

• A project on applied optimization counting 30% of the course. The project can be chosen from different areas (process design, process operation, automation, environmental engineering, economics, etc.). The project report should be written in article format
• A final test counting 70 % of the course.

To pass the course as a whole, the candidate must pass both the project and the final test.

Minor adjustments may occur during the academic year, subject to the decision of the Dean