Fundamentals of Optimization: An Interdisciplinary and Applied Approach
Course description
How can a forestry manager allocate resources in the best possible way to sustain forest health and yield? How can an economist determine the most effective way to distribute limited resources to meet market demand? How can an engineer develop a system that achieves the best trade-off between cost, efficiency, and safety? How can a biologist structure an experiment to get the most valuable results while considering time and budget constraints?
All of these are optimization problems—challenges where the goal is to make the best possible decision while balancing trade-offs and limitations.
Optimization is often misunderstood—many think it simply means improving something, but in reality, it follows a structured approach to decision-making based on goals and specifications. At its core, optimization is a mathematical process that involves maximizing or minimizing a function while considering constraints to find the best possible outcome.
This course demystifies optimization, making it accessible to Ph.D. students across multiple disciplines, including forestry, agriculture, economics, engineering, biology and beyond. Students will learn how to correctly define and approach optimization problems, even without a strong mathematical background.
Through practical examples and hands-on techniques, students will explore key concepts such as linear and nonlinear programming, classical optimization algorithms, multi-objective optimization and metaheuristic approaches. These methods can enhance efficiency, resource allocation, and support better decision-making across various fields.
By the end of the course, students will have a solid understanding of the fundamentals of optimization and gain basic tools to approach and apply key techniques in their research and professional work.
Course facts
P000148, Fundamentals of Optimization: An Interdisciplinary and Applied Approach, 4.5 Hp
Print syllabus
Syllabus
Level
Third cycle
Subject
Technology
Grading Scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Course language
English
Entry Requirements
Objectives
On completion of the course, the student will be able to:
1.- Describe what an optimization problem is and identify its key components (objective function, decision variables, and constraints).
2.- Classify different types of optimization problems, such as linear vs. nonlinear and single vs. multi-objective.
3.- Formulate simple optimization problems from real-world scenarios and from their own research projects.
4.- Use computational tools to apply basic optimization techniques and solve simple problems.
5.- Interpret the results of optimization models, understanding trade-offs and constraints.
Content
The course introduces the fundamental concepts of optimization, focusing on real-world applications in various disciplines such as forestry, economics, engineering, biology and beyond. Students will learn how to formulate, analyse, and solve basic optimization problems while considering constraints, trade-offs, and decision-making processes.
The course covers:
• Fundamental principles of optimization modelling.
• Types of optimization problems: linear vs. nonlinear, single vs. multi-objective.
• Introduction to common solution methods, including classical algorithms and meta-heuristic approaches.
• Hands-on implementation using computational tools and coding.
• Interpretation of optimization results and limitations.
The course includes a mix of lectures, practical coding sessions with real-world examples from different disciplines, fostering a broader interdisciplinary perspective on optimization. Alongside lectures, students will complete a series of assignments that gradually build their skills, from connecting optimization to their own field, to solving practical problems, and exploring trade-offs in decision making. The course concludes with a final project in which each student formulates and analyses an optimization problem relevant to their research.
Examination Formats and Requirements for Passing the Course
1\.- Approved assignments. 2.- Approved final project.
Responsible Department/Equivalent
Department of Forest Biomaterials and Technology
Supplementary information
Other Information
All costs for board and lodging should be covered by the student.