Summary: A stochastic programming approach for
supply chain network design under uncertainty
Tjendera Santoso, Shabbir Ahmed
, Marc Goetschalckx, Alexander Shapiro
School of Industrial & Systems Engineering,
Georgia Institute of Technology, 765 Ferst Drive, Atlanta, GA 30332.
June 16, 2003
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.
Keywords: Facilities planning and design; Supply chain network design; Stochastic program-
ming; Decomposition methods; Sampling.