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Applied Operations Research
Provider: Faculty of Science

Activity no.: 5554-21-07-31 
Enrollment deadline: 06/09/2021
PlaceDepartment of Mathematical Sciences
Universitetsparken 5, 2100 København Ø
Date and time06.09.2021, at: 08:00 - 12.11.2021, at: 16:00
Regular seats50
ECTS credits7.50
Contact personNina Weisse    E-mail address: weisse@math.ku.dk
Enrolment Handling/Course OrganiserGiovanni Pantuso    E-mail address: pantuso@math.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 1
Scheme groupC
Exam requirementsThe students must hand in a project report that must be approved in order to qualify for the oral exam.
Exam formOral examination, 30 minutes
Exam form30 minutes preparation, 30 minutes oral examination including grade determination
Exam detailsThe course will contain a mandatory group project and each participant will be assigned to one project which is to be done during the course. The students will be arranged in groups of 4-5 people. The projects will be open ended - no correct answer will exist! The aim is to experiment creatively and learn the challenges and inner workings of state space models and particle methods under supervision. The students will present their work and results (blackboard/projector but no typed text expected). The assessment will not be strict, the point is to get the students trying things out as a research project.
Exam aidsAll aids allowed
Grading scalePassed / Not passed
Course workload
Course workload categoryHours
Lectures14.00
Preparation25.00
E-learning40.00
Practical Training14.00
Project work50.00
Eksamensforberedelse62.00
Exam1.00

Sum206.00


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)

C. Decomposition techniques for mathematical programming problems: the course will illustrate central decomposition techniques for mathematical programs with complicating structures or large-scale problems

D. Implementation of advanced solution methods: Implementation of advanced solution methods using the software introduced during the course

E.Introduction to heuristics: Introduction to heuristic methods for finding solutions to complex optimization problems.

F. Project work: The students will apply the content learnt throughout the course for building optimization applications that solve a given real-life problem.

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.

Literature
Lecture notes and tutorials provided by the teacher.

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

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