Operations Research in R (IND670)

Content

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th for the autumn semester.

This course teaches tools to solve practical economic decision problems.

We will use the statistical software R to both apply and develop relevant optimization methods to solve different decision problems. The course will teach both the application of these methods, and how they work to solve different decision problems.

For example, you will learn:

• How to find optimal transportation routes in a logistic network, including optimal bus route planning and optimal delivery routes.
• How to manage inventories of goods, when to place an order and how much to order.
• When to optimally replace a machine.
• How to optimize prediction models using gradient descent.
• How to optimize customer service in a queuing systems.
• How to allocate a budget across multiple projects under given constraints.
• + more...

We will get into the details of how and why various optimization algorithms work for different types of problems.

After finishing the course you will be comfortable and competent in using R for quantitative analysis.

Learning outcome

Knowledge

After completing the course, the student should know:

• Linear, integer and non-linear mathematical programming relevant to business decision problems
• Conditions under which different models can be used and their limitations
• Basic knowledge of solution algorithms
• Network models
• Queuing Theory
• Inventory Theory
• Transportation problems
• Dynamic programming
• Simulation and forecasting of decision problems with uncertainty
• How to identify decision problems where Operational Analysis is relevant
• Fundamental economic tradeoffs and constraints in decision problems

Skills

After completing the course, the student should be able to:

• Identify from a general problem setup how to implement and solve a decision problem using linear, integer and non-linear mathematical programming
• Give economic interpretations of the model solutions and what they imply for the costs and constraints facing businesses in decision problems
• Solve basic queuing, inventory and transportation problems
• Solve general network problems
• Perform simulation and forecasting analysis of decisions under uncertainty
• State limitations of different models for different problems

General competence

After completing the course, the student should be able to communicate:

• How a business decision problem can be formulated as a mathematical decision problem
• The different types of models available and the conditions under which they apply
• How the models can be practically implemented and solved
• The fundamental economic constraints and trade-offs businesses face in their decision problems

Required prerequisite knowledge

Recommended prerequisites

Exam

Coursework requirements

Course teacher(s)

Course coordinator:

Head of Department:

Method of work

(lectures, group projects, laboratory exercises):

The course includes lectures and computer lab work

4 hours teaching per week.

Open for

Course assessment

Literature