The 17th International Symposium on Mathematical Programming
Extending algebraic modelling languages for Stochastic Programming.
Abstract: The algebraic modelling languages (AML) have gained wide acceptance and use by researchers and
practitioners of Mathematical Programming. At a simple level, stochastic programming (SP)
models can be defined by a deterministic equivalent representation using an AML. Unfortunately,
this leads to very large model data instances. We propose a direct approach in which the random
values of the model coefficients and the stage structure of the decision variables and constraints
are "overlaid" on the underlying algebraic (core) model of the SP problems. This leads to not
only a natural definition of the SP model: the resulting generated instance is also a compact
representation of the otherwise large explicit representation. This design is presented as
SMPL: a stochastic extension of the MPL language. Our work in progress illustrates the
generic aspect of this concept, whereby we are able introduce similar "stochastic" constructs
to the AMPL syntax and thereby define an extension which we call SAMPL.
Author: Prof. Gautam Mitra
Brunel University
Department of Mathematical Sciences