
Many planning and control problems involve a sequence of decisions
that are made over time. The initial decision is followed by
a second, the second by a third, and so on. The process continues
perhaps infinitely. Because the word dynamic describes situations
that occur over time and programming is a synonym for planning,
the original definition of dynamic programming was "planning
over time." In a limited sense, our concern is with decisions
that relate to and affect phenomena that are functions of time.
This is in contrast to other forms of mathematical programming
that often, but not always, describe static decision problems.
As is true in many fields, the original definition has been
broadened somewhat over the years to connote an analytic approach
to problems involving decisions that are not necessarily sequential
but can be viewed as such. In this expanded sense, dynamic programming
(DP) has come to embrace a solution methodology in addition
to a class of planning problems. It is put to the best advantage
when the decision set is bounded and discrete, and the objective
function is nonlinear.
This section is primarily concerned with modeling of deterministic,
discrete systems. Modeling requires definitions of states and
decisions, as well as the specification of a measure of effectiveness.
To model with dynamic programming, an abstraction or reduction
of complexity from the real world problem is necessary. There
are two reasons for this, one practical and the other computational.
From a practical point of view, it is rarely possible to identify
and evaluate all the factors that are relevant to a realistic
decision problem. Thus the analyst will inevitably leave out
some more or less important descriptors of the situation. From
a computational point of view, only problems with relatively
simple state descriptions will be solvable by dynamic programming
algorithms. Thus abstraction is necessary in order to arrive
at a formulation that is computationally tractable. Often a
particular problem may have several alternative representations
in terms of state and decision variables. It is important that
the analyst realize that some formulations require more computation
than others do, and hence choose the most manageable representation.
Dynamic programming has been described as the most general
of the optimization approaches because conceivably it can solve
the broadest class of problems. In many instances, this promise
is unfulfilled because of the attending computational requirements.
Certain problems, however, are particularly adaptable to the
model structure and lend themselves to efficient computational
procedures; in cases involving discontinuous functions or discrete
variables, dynamic programming may be the only practical solution
methodology.
The example for this section introduces the notation for dynamic
programming with an example investment problem. We briefly outline
the solution approach to provide motivation for the modeling
notation. The textbook describes several additional problem
classes with their dynamic programming models.
