Two-Stage Stochastic Linear Programming with Recourse: A Characterization of Local Regions Using Response Surface Methodology von David T. Mills: New Paperback (2012)

Beschreibung

Bestandsnummer des Verk�ufers 6666-IUK-9781249586760

Bibliografische Details

Titel: Two-Stage Stochastic Linear Programming with Recourse: A Characterization of Local Regions Using Response Surface Methodology
Verlag: BiblioScholar 2012-10-03
Ver�ffentlichungsdatum: 2012
Sprache: Englisch
ISBN-10: 1249586763
ISBN-13: 9781249586760
Bindung: Paperback
Zustand: New

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Inhaltsangabe

The LP recourse problem applies to two-stage optimization problems where uncertainty in resource availability of the second stage hinders informed decision making. The recourse function affords a way to compensate "later" for an error in prediction "now." The literature provides a rich body of work on the optimization of such problems, but little research has been accomplished regarding the characterization of the surface in the local region of optimality, in particular sensitivity analysis. A decision maker faced with considerations other than the modeled objective function must be presented with a way to estimate the impact of operating at non-optimal decision variable values. This work develops and demonstrates a technique for characterizing the surface using response surface methodology. Specifically, the flexibility and utility of RSM techniques applied to this class of problems is demonstrated, and a methodology for characterizing the surface in the local region using a low-order polynomial is developed.

Rese�a del editor

The LP recourse problem applies to two-stage optimization problems where uncertainty in resource availability of the second stage hinders informed decision making. The recourse function affords a way to compensate "later" for an error in prediction "now." The literature provides a rich body of work on the optimization of such problems, but little research has been accomplished regarding the characterization of the surface in the local region of optimality, in particular sensitivity analysis. A decision maker faced with considerations other than the modeled objective function must be presented with a way to estimate the impact of operating at non-optimal decision variable values. This work develops and demonstrates a technique for characterizing the surface using response surface methodology. Specifically, the flexibility and utility of RSM techniques applied to this class of problems is demonstrated, and a methodology for characterizing the surface in the local region using a low-order polynomial is developed.

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