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

@article{citeulike:1127376, 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.}, author = {Santoso, T. and Ahmed, S. and Goetschalckx, M. and Shapiro, A.}, citeulike-article-id = {1127376}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.ejor.2004.01.046}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0377221704002292}, citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6VCT-4CDHJ1P-2/2/6eb7c7d53a11c930b01ebebbd42c17ba}, day = {16}, doi = {10.1016/j.ejor.2004.01.046}, issn = {03772217}, journal = {European Journal of Operational Research}, keywords = {network\_design, stochastic\_programming, supply\_chain}, month = {November}, number = {1}, pages = {96--115}, posted-at = {2009-10-27 21:22:37}, priority = {2}, title = {A stochastic programming approach for supply chain network design under uncertainty}, url = {http://dx.doi.org/10.1016/j.ejor.2004.01.046}, volume = {167}, year = {2005} }

TY - JOUR ID - citeulike:1127376 L3 - citeulike-article-id:1127376 N2 - 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. IS - 1 JF - European Journal of Operational Research SN - 03772217 EP - 115 TI - A stochastic programming approach for supply chain network design under uncertainty VL - 167 SP - 96 KW - network_design KW - stochastic_programming KW - supply_chain AU - Santoso, T AU - Ahmed, S AU - Goetschalckx, M AU - Shapiro, A PY - 2005/11/16/ UR - http://dx.doi.org/10.1016/j.ejor.2004.01.046 ER -