Applied Operations Research

Operations Research, and particularly Mathematical Programming, is a widely used methodology for optimization and decision-making. It is of central importance in the industry, with applications ranging from logistics to finance, from production planning to energy. It is also of vital importance in emerging areas such as machine learning and for addressing current societal issues such as the green transition.

The course will introduce the students to the practical aspects of Operations Research. The objective is to provide the competencies necessary to work on Operations Research projects in practice. The course will go through the OR scientist's "toolbox", that is, a minimal set of mathematical and software tools required for developing OR solutions. In addition, it provides significant hands-on experience by means of several exercises and project work on real-world applications.

The course will cover the following content:

• A. Using mathematical programming to model real-life decision problems: Given a description of a real-world optimization problem, the course will discuss how to formulate an appropriate mathematical programming problem and what are the issues involved in this phase.
• B. Using general-purpose programming languages for advanced interaction with optimization solvers: The course will introduce the students to the usage of one or more general-purpose programming languages (e.g., Java, Python, C++) for advanced interaction with state-of-the-art solvers (e.g., Cplex, Gurobi).
• B. Decomposition techniques for mathematical programming problems: Very often, industrial optimization problems are challenging due to, e.g., complicating mathematical structures or very large-scale decisions (i.e., an extremely large number of interrelated elementary decisions). The course will discuss how to handle such challenging optimization problems using decomposition techniques that break them down into smaller and easier to treat problems.
• E. Implementation of advanced solution methods: The course will teach the students how to implement decomposition techniques using the software introduced during the course.
• F. Introduction to heuristics: The course introduces heuristic methods, that is, techniques for finding quick solutions to complex optimization problems, though without guarantee of optimality.
• G. Project work: The students will apply their competencies in project work describing real-world optimization tasks from, e.g., logistics, finance, energy, as well as in several practical exercises.