2003-07-07Buch DOI: 10.18452/8297
A stochastic programming approach for supply chain network design under uncertainty
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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