This course provides an introduction to how business decision problems can be analyzed using mathematical models and computer tools. Some examples of decision problems that will be covered are:
- How to find an optimal product mix when resources are scarce?'
- How to optimally select suppliers in a tender auction?
- How to construct an optimal work schedule when demand varies over time?
- How to create an optimal investment plan when supply of capital is limited?
- How to determine an optimal portfolio of stocks with different levels of return and risk?
- How to forecast demand based on historical data?
- How to utilize a marketing budget efficiently?
- How to determine an optimal inventory policy, i.e., how often and how much should we refill the inventory?
- How much should we order when demand for a product is uncertain?
- How to determine the sequence and allocation of tasks along a production line with multiple work stations?
- How to determine an optimal transportation plan for a supply chain?
- How to determine the optimal location of production and storage facilities in a supply chain?
The choice of models in a particular situation depends on the properties of the decision problem that we are analyzing. All the models that will be studied can be analyzed using computer tools, and in this course we will be using Analytic Solver, which comes as an add-in in Excel. We will start with linear models (linear programming), which can be solved and analyzed relatively easily. Later in the course we will study problems with either/or-decisions, which requires models with integer variables, as well as non-linear decision models. Throughout the course we will emphasize interpretation of analysis results, as well as practical implications for businesses.
An important topic in our course is handling of uncertainty in decision situations. We will look at how we can make good forecasts, as well as how simulation models can be used to evaluate consequences of different decision alternatives under uncertainty. We will also study how risk attitudes can affect the choice between decision alternatives, e.g., how risk averse decision making can be modeled. Finally, we will look at how the value of additional information can be computed when future outcomes are uncertain, and how decision trees can be used to structure complex decision situations.