Mathematical Optimization is a 4-hour course.
What you will learn
- How to recognize and formulate real-world decision tasks as optimization problems
- How to automate decision processes
- How to use data to prescribe better ways to do things
- How to assess what existing technology can do for solving the real-world optimization problems that arise in your business.
How the course adds value to your organisation
Mathematical optimization provides a systematic and rigorous approach to decision-making, enabling organizations to make informed choices that lead to efficiency, cost savings, and improved overall performance across various domains such as: resource allocation, supply chain management, production planning and scheduling, inventory management, project management, financial portfolio optimization, network design and routing, facility location and layout, energy optimization, employee scheduling, marketing and pricing strategies.
Your profile
The course is for computer scientists and for analysts with an educational background in data science, mathematics, economics, or business administration.
The course aims at showing how collected data can be used not only to describe and predict current and future processes, but also to change how things are done so that usage of resources is minimized and desired goals are achieved. We will focus on recognizing these opportunities in different real-life applications.
Decision-making tasks involving determining optimal actions can be formulated mathematically as optimization problems and solved to provable optimality with existing software (for example CPLEX, Gurobi, Google OR-Tools, etc.). Rather than feeding your data into standard machine learning models, we will see how we can model mathematically your specific data from applications such that these models can lead us to take optimal decisions.
The course is taught in Danish or English, by further agreement.
Topics covered
- Examples of optimization problems arising in logistics and manufacturing
- Overview of available technologies to solve optimization problems, for example integer and linear programming, constraint programming and heuristics
- Formulation of decision tasks as optimization problems using a formal language, i.e. the model which can be understood and solved by existing solving technologies
- Implementation of models in a modeling language: MiniZinc, Pyomo or similar
Participants can contribute to the selection of the technology in focus according to their industrial needs.
The course is taught by Associate Professor Marco Chiarandini from the Department of Mathematics and Computer Science, who has 20 years' experience in the field of discrete optimization with applications in the private and public sectors.
His main research revolves around logistics and production planning and scheduling with techniques from operations research and artificial intelligence.
DKK 3,000 exclusive of VAT.
Do you have any questions
Contact Study Administrator Michael Christensen by e-mail at michr@sdu.dk or by phone at+45 6550 2542.