A stochastic programming approach for supply chain network design under uncertainty

Tjendera Santoso; Shabbir Ahmed; Marc Goetschalckx; Alexander Shapiro

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SPEPS Preprint

Author(s):	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
Submission Date:	18.06.2003
Series Title:	Stochastic Programming E-Print Series (SPEPS)
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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Abstract (eng):

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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