On the formulation of stochastic linear programs using algebraic modelling languages

Gassmann, Horand I.; Ireland, Alice

doi:10.1007/bf02187642

Cited by 28 publications

(18 citation statements)

References 28 publications

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“…Gassmann and Ireland (Gassmann and Ireland 1996) expand this concept in their work. This second class of problems, however, introduces various difficulties in the model specification using algebraic modelling languages and in terms of the solution process, in particular when some of the random parameters are continuously…”

Section: Distribution Based Problemmentioning

confidence: 98%

“…In this section we consider the family of models which is now well established and come under the broad heading of optimum decision making under uncertainty. We follow the classification of stochastic programming problems introduced by (Gassmann and Ireland 1996). We make a small extension of their categorisation by adding the Expected Value models as a subclass of the distribution problems leading to a working taxonomy shown in Figure 1.…”

Section: Taxonomy Of Sp Modelsmentioning

confidence: 99%

“…In (Gassmann and Ireland 1996), the authors also propose extension to the AMPL modelling language, mainly for the definition of probability distributions of the random parameters. Again, a language construct random is introduced in distribution-based recourse problems to identify the random parameters and the variables which depend on these.…”

Section: Proposed Approachesmentioning

confidence: 99%

“…A number of researchers have proposed extensions to algebraic languages for the formulation of such class of problems (Fourer 1996;Gassmann and Ireland 1996;Fourer 2001;Gay 2001).…”

Section: Modelling Distribution Based Recourse Problemsmentioning

confidence: 99%

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Extending Algebraic Modelling Languages for Stochastic Programming

Valente

Mitra

Sadki

et al. 2009

INFORMS Journal on Computing

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The algebraic modelling languages (AML) have gained wide acceptance and use in MathematicalProgramming by researchers and practitioners. At a basic level, stochastic programming models can be defined using these languages by constructing their deterministic equivalent. 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 deterministic (core) model of the SP problems. This leads not only to a natural definition of the SP model: the resulting generated instance is also a compact representation of the otherwise large problem data. The proposed constructs enable the formulation of two stage and multistage scenario based recourse problems, as well as chance constrained problems. This design is presented as a stochastic extension of the AMPL language which we call SAMPL.

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Section: Distribution Based Problemmentioning

confidence: 98%

Section: Taxonomy Of Sp Modelsmentioning

confidence: 99%

Section: Proposed Approachesmentioning

confidence: 99%

“…A number of researchers have proposed extensions to algebraic languages for the formulation of such class of problems (Fourer 1996;Gassmann and Ireland 1996;Fourer 2001;Gay 2001).…”

Section: Modelling Distribution Based Recourse Problemsmentioning

confidence: 99%

See 2 more Smart Citations

Extending Algebraic Modelling Languages for Stochastic Programming

Valente

Mitra

Sadki

et al. 2009

INFORMS Journal on Computing

View full text Add to dashboard Cite

show abstract

“…With few exceptions e.g. Kall andMayer 1996 andGassmann andIreland 1996 , there has been relatively little activity in this important area. It is unlikely that stochastic programming will attain its potential without the development of systems which allow easy interactions between stochastic models and stochastic programming algorithms.…”

Section: S Sen 7 Computational Issues and Challengesmentioning

confidence: 99%