Applied Operations Research

Course content

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.

MSc Programme in Mathematics-Economics


Learning outcome

At the end of the course the student should have:

  • gained knowledge
    • of common usage of continuous and integer variables for translating real-world decision problems into mathematical programming problems
    • of advanced solution methods for probles with complicating structures
    • of the features of state-of-the-art optimization software
    • of the concepts used in heuristic methods


  • acquired skills to:
    • translate the description of real-life optimization problems into suitable mathematical programming problems
    • assess the quality of a mathematical formulation
    • select a suitable solution method for a given mathematical problem
    • implement solution methods by means of a general-purpose programming language and/or state-of-the-art solvers


  • obtained the competences necessary to
    • structure a real-world optimization problem and provide a suitable mathematical description
    • select a suitable approach to solve a mathematical problem and justify the choice
    • make the choice of software necessary for a given optimization task
    • develop software products capable of handling an optimization task, possibly by implementing advanced solution methods.

2 x 2 hours of lectures or tutorials and 2 hours of exercises or project work supervision per week for 7 weeks.

Lecture notes and tutorials provided by the teacher.

Operations Research 1 (OR1) or similar.
Introduction to Numerical Analysis (NumIntro).

It is also advised, but not necessary, to take this course before other advanced Operations Research courses. Academic qualifications equivalent to a BSc degree is recommended.

Feedback by final exam (In addition to the grade)

Lecturer's oral or written feedback on assignments. Lecturer's feedback on final exam.

7,5 ECTS
Type of assessment
Oral examination, 30 minutes
Type of assessment details
30 minutes oral examination with 30 minutes preparation time.
Exam registration requirements

The students must hand in a project report that must be approved in order to qualify for the oral exam.

Only certain aids allowed

During the preparation time all written aid is allowed.

During the examination no written aid is allowed.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.

As the ordinary exam, conditional on the approval of the project work. If the project was not approved before the ordinary exam it must be resubmitted at the latest three weeks before the beginning of the re-exam week.

Criteria for exam assessment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 14
  • Preparation
  • 25
  • Practical exercises
  • 14
  • E-Learning
  • 40
  • Practical Training
  • 12
  • Project work
  • 40
  • Exam Preparation
  • 60
  • Exam
  • 1
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
No limit
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Giovanni Pantuso   (2-6a73437064776b316e7831676e)
Saved on the 28-02-2023

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