A Stochastic Programming Approach for Supply Chain Network Design Under Uncertainty
- Topics:
- Modeling
- Tags:
- Enterprise Software,
- Networking,
- Programming,
- Software,
- Solution Methodology,
- Supply Chain,
- Supply Chain Management (SCM)
- Source:
- Georgia Institute of Technology
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Overview: The 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. The solution methodology integrates a recently proposed sampling strategy, the Sample Average Approximation 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.
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Format: PDF & WORD | Date: Sep 2003 | Pages: 26
