Perhaps more aptly titled "Optimization for the Management Student," this course is designed to teach students the basic optimization tools and analytic problem solving skills required for decision making in business. We will learn how to:
- Structure a decision problem: identifying the objective, decision alternatives (i.e., outputs), input parameters, and sources of uncertainty.
- Build a mathematical model to formalize the decision problem: We will learn about
- Optimization models (linear, nonlinear) - for resource allocation (how to utilize available resources optimally)
- Decision tree models - for multiperiod sequential decision making
- Simulation models - for risk analysis and incorporating uncertainty in problem parameters.
- Analyze model solution: Is the decision fairly robust, or very sensitive to the input parameters of the model ? What is the managerial interpretation of the model solution?
- Use Microsoft Excel as a platform for model building, solution, and analysis: While spreadsheets are somewhat limited in the size and nature of models we can build, they are a useful medium for learning the decision modeling concepts mentioned above without the large time investment of learning a general purpose programming language (like Python). In addition to standard Excel tools such as Goal Seek and Data Table, we will use add-ons such as Tornado charts, Solver, SolverTable, Precision Tree, and @RISK. This is NOT a course for learning Excel, the students will be expected to go through a introductory tutorial of Excel before the first lecture.
The lectures will be structured as a brief introduction to an optimization model (its strengths, weaknesses) followed by an interactive discussion of a few (3-5) chosen toy problems from business areas including operations, marketing, finance, and strategy. We will develop the optimization models for these problems and implement them in Excel.