Most practical decision problems involve uncertainty.
Stochastic programming is the study of procedures for decision making under uncertainty over time.
The uncertainty can be in the model's parameters or in the model itself. Parameters may be uncertain because of lack of reliable data, measurement errors, future and unobservable events, etc. High-performance workstations and PCs are used to enable exact and approximate algorithms to determine robust decisions that hedge against future uncertainty. Then, as the uncertainty becomes known period by period, resource decisions responding to the new information can be made. There are many areas where significant applications of stochastic programming have been made.