|edoc-Server der Humboldt-Universität zu Berlin|
Tjendera Santoso, Georgia Institute of Technology|
Shabbir Ahmed, Georgia Institute of Technology
Marc Goetschalckx, Georgia Institute of Technology
Alexander Shapiro, Georgia Institute of Technology
|Title:||A stochastic programming approach for supply chain network design under uncertainty|
|Date of Acceptance:||07.07.2003|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-10059117)|
|Keywords (eng):||Stochastic programming, Facilities planning and design, Supply chain network design, Decomposition methods, Sampling|
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|This paper proposes a stochastic programming model and solution algorithm for solving sup-ply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the Sample Average Approximation scheme, with an accelerated Benders de-composition algorithm to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the stochastic model as well as the efficiency of the proposed solution strategy.|
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