  Decision rules offer a rich and tractable framework for solving certain
classes of multistage adaptive optimization problems. Recent literature has
shown the promise of using linear and nonlinear decision rules in which
wait-and-see decisions are represented as functions, whose parameters are
decision variables to be optimized, of the underlying uncertain parameters.
Despite this growing success, solving real-world stochastic optimization
problems can become computationally prohibitive when using nonlinear decision
rules, and in some cases, linear ones. Consequently, decision rules that offer
a competitive trade-off between solution quality and computational time become
more attractive. Whereas the extant research has always used homogeneous
decision rules, the major contribution of this paper is a computational
exploration of hybrid decision rules. We first verify empirically that having
higher uncertainty resolution or more linear pieces in early stages is more
significant than having it in late stages in terms of solution quality. Then we
conduct a comprehensive computational study for non-increasing (i.e., higher
uncertainty resolution in early stages) and non-decreasing (i.e., higher
uncertainty resolution in late stages) hybrid decision rules to illustrate the
trade-off between solution quality and computational cost. We also demonstrate
a case where a linear decision rule is superior to a piecewise-linear decision
rule within a simulator environment, which supports the need to assess the
quality of decision rules obtained from a look-ahead model within a simulator
rather than just using the look-ahead model's objective function value.
