BEA530 Optimization

• Topics

  The course introduces operations research, primarily optimization, with a focus on model classes and the formulation of the corresponding mathematical models. There will be minimal focus on solution techniques. Classes that will be covered include network flows, facility location, facility layout, vehicle routing, city logistics, emergencies, network design, games, and inventory planning.

• Learning outcome

  Knowledge

  Upon completion the student

  • has advanced knowledge about how mathematical programming can be used to analyze decision problems.
  • can understand whether or not a resulting model is easy or hard to solve
  • can formulate optimization problems related to decision problems they have not faced before.

  Skills

  Upon completion the student

  • can argue coherently about what a mathematical model expresses and relate that to the underlying decision problem
  • can write referee reports on problem-oriented optimization articles

  General competence

  Upon completion the student

  • can apply his/her knowledge and skills to formulate and understand optimization problems, and have basic knowledge about how they can be solved.
  • can see how optimization models can fit into decision problems that do not at first glance appear to be optimization problems. 

• Teaching

  There will be 12 sessions each of 2*45 minutes. Each session will start with two student presentations on articles handed out the previous week (about 30 minutes each), followed by a 30 minutes lecture on next week's theme. Each student will need four approved presentations. 

• Restricted access

  • PhD candidates at NHH
  • PhD candidates at Norwegian institutions
  • PhD candidates at other institutions
  • PhD candidates from the ENGAGE.EU alliance
  • Motivated master’s students at NHH may be admitted after application, but are subject to the approval from the course responsible on a case-by-case basis.

• Recommended prerequisites

  We expect the background of the students to be very different. It is therefore necessary for those who have no background in optimization modeling to read some background material. This material will be provided. 

   

  The course does not focus on algorthmic work, and no specific background is needed. 

• Compulsory Activity

  It is mandatory to be present in all classes in the course. Each week a new class of models is presented, and two or more articles in that class will be handed out. The articles will then be presented the following week by two or more students. All students must have four presentations approved. 

  At the end of the semester, each student must hand in a referee report on one of the papers they presented during the semester. Details on how to write referee reports will be presented to the students beforehand. 

• Assessment

  There will be an oral examination where each student must present one of the articles that they read, but did not present, during the semester. They will be informed about which article to present two days before the examination. The presentation itself will take 30 minutes, just as in class. It will be followed by questions.

• Grading Scale

  Pass - fail

• Computer tools

  The course is not implementation oriented, but students may find it useful to use AMPL as a modeling framework when preparing presentations. AMPL can the be used to solve simple versions of the problem at hand. 

• Retake

  Re-take is offered the semester after the course was offered for students with valid compulsory activities (work requirements). Additionally, the students must fulfill one of the two requirements listed below in order to be eligible for re-take:

  • Students who, at the original exam failed or got a grade below C
  • Students who were sick on the day of the exam and has provided a valid sick note ("sykemelding")