Profile of the course
Decision Science deals with quantitative methods and techniques to support decision processes. The course Decision Science 1 (ORL20306) provides a solid base for formulating and solving Linear Programming models, (Mixed) Integer Linear Programming models, an introduction to Multi-criteria Decision Making, and Dynamic Programming. Decision Science 2 broadens and deepens the knowledge and skills acquired in Decision Science 1 and presents important topics that were not presented in Decision Science 1.
The topics in Decision Science 2 can be classified into three groups:
1. Multi-criteria decision making
Ways of dealing with problems that have several, mostly conflicting, objectives. This topic also comprises Risk and Uncertainty, in order to incorporate into the decision making process the personal judgements of the decision maker about uncertainties and outcome-values.
Many systems are so complex that you cannot optimise them in a straightforward, analytical way. Simulation increases the understanding of such a system by building a model of reality, and analysing its behaviour.
3. Approximation methods (heuristics)
In many cases problems are too big or too complex to find an optimal solution in a reasonable amount of time. In those cases we can use approximation methods (heuristics) that find relatively good solutions in a relatively short amount of time.
In Decision Science 2 three different chair groups participate:
- Operations Research and Logistics (ORL)
- Information technology (INF)
- Business Economics (BEC)
The methods and techniques will be demonstrated with examples from e.g. the milk chain, production planning, pest control, investment problems.