Decision Analysis 2 (IND670)

Content

This course will teach students to develop and use mathematical models to organize and optimize business activities, normally under conditions of scarcity of resources. Decision Analysis consists of limiting and defining a decision problem, formulating a mathematical model of the problem, calculating an optimal solution, and analysing the implications of the solution.

Students will apply the tools in the course to relevant practical problems for business activities such as transportation problems, purchasing decisions, work scheduling and inventory management. The course will go in depth on topics covered in the course IND520 Decision Analysis using Excel, in addition to introducing new relevant topics.

Content:

• Mathematical models to organize and optimize business activities
• Solution algorithms for different model
• Practical implementation and analysis of different models
• Understanding when and how different models can be applied to different decision problems
• Build understanding and intuition of fundamental economic constraints and costs facing businesses in decision problem

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

Exam

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