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2016/2017  KAN-CMATV1063U  Mathematical Optimization: Models, Methods and Applications

English Title
Mathematical Optimization: Models, Methods and Applications

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 100
Study board
Study Board for BSc/MSc in Business Administration and Management Science, MSc
Course coordinator
  • Dolores Romero Morales - Department of Economics (ECON)
Main academic disciplines
  • Finance
  • Economics
Last updated on 03-03-2016
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: After completing the course, students should:
.
  • Appreciate the important role of Optimization in Decision Making.
  • Display knowledge of Optimization Theory and apply this knowledge in areas such as Portfolio Optimization, Resource Allocation, and Project Management
  • Be confident users of package computer programs that are widely used in industry for Optimization
  • Appreciate the wider implications of multiple objectives and uncertainty in Optimization and the related need for solutions that are both flexible and robust
Examination
Mathematical Optimization: Models, Methods and Applications:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

This course aims to enhance your theoretical knowledge of and practical skills in Optimization tools. The course uses computer software to illustrate how to apply the methodologies we introduce. The course is multidisciplinary in nature with links to areas such as economics, finance, marketing, and operations management. The structure of the course is as follows:

 

          Mixed Integer Linear Programming

  • Modelling
  • Methods
  • Applications

          Nonlinear Programming

  • Modelling
  • Methods
  • Applications

          Multiobjective Optimization

  • The Pareto Frontier
  • Methods
  • Applications

          Stochastic and Robust Optimization

  • Modelling
  • Methods
  • Applications

          Heuristics

  • Constructive
  • Matheuristics
  • Metaheuristics

 

The course’s development of personal competences:

 

During the course, and through a hands-on approach supported by optimization theory, students will develop quantitative skills needed for Decision Making, as well as learn to appreciate the implications of multiple objectives and uncertainty in Decision Making and the need for flexible and robust solutions.
Teaching methods
Lectures, Exercises, Demos, Computer Workshops
Student workload
Preparation 104 hours
Classes 30 hours
Exam 72 hours
Further Information

Main academic disciplines: Optimization, Economics, Finance
Expected literature

Preparatory Reading

 

Hillier, F.S. and Lieberman, G.J. (2015), Introduction to Operations Research. 10th Edition. McGraw-Hill.

 

Additional Reading

 

Fourer, R., Gay, D. and Kernighan, B. (2002). A Modeling Language for Mathematical Programming. 2nd Edition. Duxbury Press.

 

Rardin, R.L. (1998) Optimization in Operations Research. Prentice Hall.

 

Williams, H.P. (2013), Model Building in Mathematical Programming. 5th Edition. John Wiley & Sons.
Last updated on 03-03-2016