ABSTRACT
This chapter explores how to achieve optimal results in the face of uncertainty using stochastic optimization models. Optimal results may be characterized as maximum expected outputs, maximum profits, minimum costs, minimum variability, or even some combination of these. We illustrate how to set up optimization problems that also consider the uncertainty of the possible results. There is often a tradeoff between risk and return, and these models can help us to improve our decision-making and get to more optimal decisions. We provide an extended example for pricing of airplane seats by class of service, including use of an efficient frontier.
