Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.ejor.2004.01.046 | European Journal of Operational Research |
Keywords | Field | DocType |
Facilities planning and design,Supply chain network design,Stochastic programming,Decomposition methods,Sampling | Sample average approximation,Mathematical optimization,Supply chain network,Decomposition method (constraint satisfaction),Sampling (statistics),Supply chain,Stochastic modelling,Stochastic programming,Mathematics,Benders' decomposition,Operations management | Journal |
Volume | Issue | ISSN |
167 | 1 | 0377-2217 |
Citations | PageRank | References |
133 | 6.93 | 17 |
Authors | ||
1 | ||