A stochastic programming approach for supply chain network design under uncertainty.

*(English)*Zbl 1075.90010Summary: This paper proposes a stochastic programming model and solution algorithm for solving supply 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 (SAA) scheme, with an accelerated Benders decomposition 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 programming; Decomposition methods; Sampling
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\textit{T. Santoso} et al., Eur. J. Oper. Res. 167, No. 1, 96--115 (2005; Zbl 1075.90010)

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