Mathematical optimization is widely used to solve challenging decision problem.

Stochastic programming is a subfield of mathematical optimimization that involves uncertainty. In a stochastic programm some or all problem parameter are uncertain but follow a known probability distribution, whereas in determinstic optimization all problem parameters are assumed to be known exactly.
The goal in stochastic programming is to find a policy that is feasible for all possible data instances and maximizes the expectation of some function of the decisions and the random variables.
Because many real-world decisions involve uncertainty, stochastic programming has found applications in a broad range of areas ranging from finance to transportation to energy optimization.

The Python library Pyomo is a great tool to solve mathematical optimization problems as it supports a wide range of problem types in mathematical optimization.

In this talk we will see how to use Pyomo to build and solve decision models when data is assumed to be known exactly.
We see different ways to include incertainty in an optimization model and how this can be implemented using the Pyomo.
Moreover we see how we can combine data driven forecasts with optimal decision making.