Advances in Linear Stochastic Programming | The Two-Level Decomposition Principle Via Dual Central Pricing

Lila Rasekh

ISBN 10: 3639218809 / ISBN 13: 9783639218800

Published by VDM Verlag Dr. Müller, 2009

About this Item

Advances in Linear Stochastic Programming | The Two-Level Decomposition Principle Via Dual Central Pricing | Lila Rasekh | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2009 | VDM Verlag Dr. Müller | EAN 9783639218800 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Seller Inventory # 101413591

Bibliographic Details

• Title: Advances in Linear Stochastic Programming | The Two-Level Decomposition Principle Via Dual Central Pricing
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2009
• Language: English
• ISBN 10: 3639218809
• ISBN 13: 9783639218800
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 254 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Multi-stage Stochastic Programming (MSP) has many practical applications for solving problems whose current resolution must be made while taking into account future uncertainty. A variety of methods exist for solving stochastic programming problems, among them are: direct methods such as simplex and interior point methods, and decomposition methods such as ACCPM (Analytic Center Cutting Plane Method), Dantzig-Wolfe and Benders decompositions, and L-shaped method. Moreover, approximation methods such as Monte Carlo simulation, as well as combinatorial heuristics are employed to solve this class of problems. In addition to extensive discussion on methods for solving multi-stage stochastic program, this book addresses two important production planning challenges namely, stochastic capacity planning; and stochastic in-house production and outsourcing planning. Problems are formulated as large-scale, Multi-stage Stochastic programs and solved implementing an innovative two-level, interior-point decomposition algorithm based on ACCPM.