  The most common approaches for solving stochastic resource allocation
problems in the research literature is 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 lookahead can be an effective
way of handling uncertainty, while enjoying the computational simplicity of a
deterministic lookahead. We present the parameterized lookahead model as a form
of policy for solving a stochastic base model, which is used as the basis for
optimizing the parameterized policy. This approach can handle complex,
high-dimensional state variables, and avoids the usual approximations
associated with scenario trees. We formalize this approach and demonstrate its
use in the context of a complex, nonstationary energy storage problem.
