Applied Operations Research

Course content

The course will introduce the students to 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 "toolbox", that is, a minimal set of (mainly software) tools required for developing OR solutions.

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: Introduction 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: the course will illustrate central decomposition techniques for mathematical programs with complicating structures or large-scale problems
  • E. Implementation of advanced solution methods: Implementation of advanced solution methods using the software introduced during the course
  • F. Introduction to heuristics: Introduction to heuristic methods for finding solutions to complex optimization problems.
  • G. Project work: The students will apply the content learnt throughout the course for building optimization applications that solve a given real-life problem.
Education

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 to 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 probgramming 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 hours of lectures and 2 hours of exercises per week for 7 weeks in addition to project work.

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.

Oral
Feedback by final exam (In addition to the grade)

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

ECTS
7,5 ECTS
Type of assessment
Oral examination, 30 minutes
30 minutes oral examination with 30 minutes preparation time.
Aid
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.
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 14
  • Preparation
  • 25
  • E-Learning
  • 40
  • Practical Training
  • 14
  • Project work
  • 50
  • Exam Preparation
  • 62
  • Exam
  • 1
  • English
  • 206