What is Stochastic Programming
written by Derek Holmes
This page gives a very simple introduction to Stochastic Programming and its uses.
Mathematical Programming
Many decision problems can be modeled using mathematical programs, which seek to maximize or minimize some
objective which is a function of the decisions. The possible decisions are constrained by limits in resources,
minimum requirements, etc. Decisions are represented by variables, which may be, for example, nonnegative or
integer. Objectives and constraints are functions of the variables, and problem data. Examples of problem data
include unit costs, production rates, sales, or capacities.
Suppose the decisions are represented by the variables (x1,x2,...xn). For example, x(i) can represent production of
the i th of n products. The general form of a mathematical program is
minimize
f(x1, x2, x3, ...., xn)
subject to
g1(x1, x2, x3, ...., xn) <= 0
..
gm(x1, x2, x3, ...., xn) <= 0
x1, x2, x3, ...., xn in X
where X is a set that be, e.g. all nonnegative real numbers. The constraints can be quite general, but linear
constraints are sufficient in many cases to capture the essence of the model.
For a good introduction to Mathematical Programming, we like
 Linear Programming and Network Flows , by Bazarra, Jarvis, and Sherali, Wiley, 1990.
Stochastic Programming
Stochastic programs are mathematical programs where some of the data incorporated into the objective or
constraints is uncertain. Uncertainty is usually characterized by a probability distribution on the parameters.
Although the uncertainty is rigorously defined, in practice it can range in detail from a few scenarios (possible
outcomes of the data) to specific and precise joint probability distributions. The outcomes are generally described in
terms of elements w of a set W. W can be, for example, the set of possible demands over the next few months.
When some of the data is random, then solutions and the optimal objective value to the optimization problem are
themselves random. A distribution of optimal decisions is generally unimplementanble (what do you tell your
boss?). Ideally, we would like one decision and one optimal objective value.
Recourse Models
One logical way to pose the problem is to require that we make one decision now and minimize the expected costs
(or utilities) of the consequences of that decision. This paradigm is called the recourse model. Suppose x is a vector
of decisions that we must take, and y(w) is a vector of decisions that represent new actions or consequences of x.
Note that a different set of y's will be chosen for each possible outcome w. The Two-Stage formulation is
minimize
f1(x) + Expected Value[ f2( y(w), w ) ]
subject to
g1(x) <= 0, ... gm(x) <= 0
h1(x, y(w) ) <= 0 for all w in W
..
hk(x, y(w) ) <= 0 for all w in W
x in X, y(w) in Y
The set of constraints h1 ... hk describe the links between the first stage decisions x and the second stage decisions
y(w). Note that we require that each constraint hold with probability 1, or for each possible w in W. The functions f2
are quite frequently themselves the solutions of mathematical problems. We don't want to make an arbirary
correction (recourse) to the first stage decision; we want to make the best such correction.
Recourse models can be extended in a number of ways. One of the most common is to include more stages. With a
multistage problem, we in effect make one decision now, wait for some uncertainty to be resolved (realized), and
then make another decision based on what's happened. The objective is to minimize the expected costs of all
decisions taken.
Solving Recourse Problems
Solving a recourse problem is generally much more difficult than the determinsitic version. The hardest part is to
evaluate the expected costs of each stage (except the first). In effect, we're trying to perform high-dimensional
numerical integration on the solutions to mathematical programs.
When the random data is discretely distributed, the problem can be written as a large deterministic problem. The
expectations can be written as finite sums, and each constraint can be duplicated for each realization of the random
data. The resulting deterministic equivalent problem can be solved using any general purpose optimization package.
(A program called STOCHTOMPS that forms linear determinisitic equivalent problems is available via anonymous
FTP.) The determinsitic equivalent models enjoy a lot of structure, so several packages have been developed to
solve them quickly
When the random data has a continuous distribution, the integrals are particularly difficult. However, you can find
upper and lower bounds to the expected recourse cost that reduce to discretely distributed stochastic programs.
(Other approximations have been developed, but not extensively tested.)
Probabilistically constrained models
In some cases, it may be more appropriate to try to find a decision which ensures that a set of constraints will hold
with a certain probability. An example might be a delivery service that experiences random demands, and wishes to
find the cheapest way to deliver its packages with a high probability.
Again, assume that x is a vector of decisions. The general form of this problem is to
minimize
f(x1, x2, x3, ...., xn)
subject to
Pr[ g1(x1, x2, x3, ...., xn) <= 0 ...
..
gm(x1, x2, x3, ...., xn) <= 0 ] >= alpha
h1(x1, x2, ... xn ) <= 0
..
hk(x1, x2, ... xn ) <= 0
x1, x2, x3, ...., xn
in X
Applications of Stochastic Programmins
Stochastic programming has been applied to a wide variety of areas. Some of the specific problems are part of the
Stochastic Programming test set. Other applications are listed below.
 Manufacturing Production Planning
 Manufacturing production capacity planning
 Electrical generation capacity planning
 Machine Scheduling
 Freight scheduling
 Dairy Farm Expansion planning
 Macroeconomic modeling and planning
 Timber management
 Asset Liability Management
 Portfolio selection
 Traffic management
 Optimal truss design
 Automobile Dealership invetory management
 Lake level management