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A Long View of Research and Practice in Operations Research and Management ScienceChallenges in Adding a Stochastic Programming/Scenario Planning Capability to a General Purpose Optimization Modeling System

A Long View of Research and Practice in Operations Research and Management Science: Challenges in... [We describe the stochastic programming capabilities that have recently been added to LINDO application programming interface optimization library, as well as how these stochastic programming capabilities are presented to users in the modeling systems: What’sBest! and LINGO. Stochastic programming, which might also be suggestively called Scenario Planning, is an approach for solving problems of multi-stage decision making under uncertainty. In simplest form stochastic programming problems are of the form: we make a decision, then “nature” makes a random decision, then we make a decision, etc. A notable feature of the implementation is the generality. A model may have integer variables in any stage; constraints may be linear or nonlinear. Achieving these goals is a challenge because adding the probabilistic feature makes already complex deterministic optimization problems even more complex, and stochastic programming problems can be difficult to solve, with a computational effort that may increase exponentially with the number of stages in the “we, nature” sequence of events. An interesting design decision for our particular case is where a particular computational capability should reside, in the front end that is seen by the user or in the computational engine that does the “heavy computational lifting.”] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Long View of Research and Practice in Operations Research and Management ScienceChallenges in Adding a Stochastic Programming/Scenario Planning Capability to a General Purpose Optimization Modeling System

Editors: Sodhi, ManMohan S.; Tang, Christopher S.
Springer Journals — Aug 27, 2010

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Publisher
Springer US
Copyright
© Springer Science+Business Media, LLC 2010
ISBN
978-1-4419-6809-8
Pages
117 –135
DOI
10.1007/978-1-4419-6810-4_8
Publisher site
See Chapter on Publisher Site

Abstract

[We describe the stochastic programming capabilities that have recently been added to LINDO application programming interface optimization library, as well as how these stochastic programming capabilities are presented to users in the modeling systems: What’sBest! and LINGO. Stochastic programming, which might also be suggestively called Scenario Planning, is an approach for solving problems of multi-stage decision making under uncertainty. In simplest form stochastic programming problems are of the form: we make a decision, then “nature” makes a random decision, then we make a decision, etc. A notable feature of the implementation is the generality. A model may have integer variables in any stage; constraints may be linear or nonlinear. Achieving these goals is a challenge because adding the probabilistic feature makes already complex deterministic optimization problems even more complex, and stochastic programming problems can be difficult to solve, with a computational effort that may increase exponentially with the number of stages in the “we, nature” sequence of events. An interesting design decision for our particular case is where a particular computational capability should reside, in the front end that is seen by the user or in the computational engine that does the “heavy computational lifting.”]

Published: Aug 27, 2010

Keywords: Optimal Policy; Stochastic Programming; Core Model; Latin Hypercube Sampling; Strike Price

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