  The most common approaches for solving multistage stochastic programming
problems in the research literature have been to either use value functions
("dynamic programming") or scenario trees ("stochastic programming") to
approximate the impact of a decision now on the future. By contrast, common
industry practice is to use a deterministic approximation of the future which
is easier to understand and solve, but which is criticized for ignoring
uncertainty. We show that a parameterized version of a deterministic
optimization model can be an effective way of handling uncertainty without the
complexity of either stochastic programming or dynamic programming. We present
the idea of a parameterized deterministic optimization model, and in particular
a deterministic lookahead model, as a powerful strategy for many complex
stochastic decision problems. This approach can handle complex,
high-dimensional state variables, and avoids the usual approximations
associated with scenario trees or value function approximations. Instead, it
introduces the offline challenge of designing and tuning the parameterization.
We illustrate the idea by using a series of application settings, and
demonstrate its use in a nonstationary energy storage problem with rolling
forecasts.
