Supply chain design under uncertainty using sample average approximation and dual decomposition
We present a supply chain design problem modeled as a sequence of splitting and combining processes. We formulate the problem as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to...
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| Veröffentlicht in: | European journal of operational research 2009-12, Vol.199 (2), p.409-419 |
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| container_title | European journal of operational research |
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| creator | Schütz, Peter Tomasgard, Asgeir Ahmed, Shabbir |
| description | We present a supply chain design problem modeled as a sequence of splitting and combining processes. We formulate the problem as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to minimize the sum of investment costs and expected costs of operating the supply chain. In particular the model emphasizes the importance of operational flexibility when making strategic decisions. For that reason short-term uncertainty is considered as well as long-term uncertainty. The real-world case used to illustrate the model is from the Norwegian meat industry. We solve the problem by sample average approximation in combination with dual decomposition. Computational results are presented for different sample sizes and different levels of data aggregation in the second stage. |
| doi_str_mv | 10.1016/j.ejor.2008.11.040 |
| format | Article |
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We formulate the problem as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to minimize the sum of investment costs and expected costs of operating the supply chain. In particular the model emphasizes the importance of operational flexibility when making strategic decisions. For that reason short-term uncertainty is considered as well as long-term uncertainty. The real-world case used to illustrate the model is from the Norwegian meat industry. We solve the problem by sample average approximation in combination with dual decomposition. Computational results are presented for different sample sizes and different levels of data aggregation in the second stage.</description><subject>Applied sciences</subject><subject>Approximation</subject><subject>Decision theory. Utility theory</subject><subject>Dual decomposition</subject><subject>Exact sciences and technology</subject><subject>Logistics</subject><subject>Mathematical programming</subject><subject>Meat industry</subject><subject>Operating costs</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Operations research</subject><subject>Sample average approximation</subject><subject>Stochastic models</subject><subject>Stochastic programming</subject><subject>Studies</subject><subject>Supply chain design</subject><subject>Supply chain design Stochastic programming Sample average approximation Dual decomposition</subject><subject>Supply chains</subject><subject>Uncertainty</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9UVGL1DAQDqLgevoHfCqCj62ZNM2m4IscnooHPqjPYTaZ7qV025q0i_vvnbLHPRqYzGT4vsmXL0K8BVmBBPOhr6ifUqWktBVAJbV8JnZg96o01sjnYifr_b5UCvYvxauceyklNNDshPu5zvNwKfwDxrEIlONxLNYxUOLdU1q4vVyKNcfxWGQ8zQMVeKaER87znKa_8YRLnMYCx1CEFQce4qfTPOW4tV-LFx0Omd485hvx--7zr9uv5f2PL99uP92XXjdmKQ9oDvXB48FYa0l30KKxwSqslfGNluhV12o-qcYEfqVB9MHoOljftA3K-ka8u85lSX9WyovrpzWNfKVTUoM2oBSD1BXk05Rzos7NieWniwPpNh9d7zYf3eajA3DsI5O-X0mJZvJPDOLFUMru7GqEtuX9wsHUrYxbyTFzaO5oaN3DcuJp7x91YvY4dAlHH_PTVAVNbXW7Sf14xRGbdo6UXPaR-EdCTOQXF6b4P9H_AAE1o8M</recordid><startdate>20091201</startdate><enddate>20091201</enddate><creator>Schütz, Peter</creator><creator>Tomasgard, Asgeir</creator><creator>Ahmed, Shabbir</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20091201</creationdate><title>Supply chain design under uncertainty using sample average approximation and dual decomposition</title><author>Schütz, Peter ; Tomasgard, Asgeir ; Ahmed, Shabbir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c456t-ba6b3bcab6888e4f19a68d82a326c540ac2f94a32256d2006aacd643d8c595a03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Applied sciences</topic><topic>Approximation</topic><topic>Decision theory. Utility theory</topic><topic>Dual decomposition</topic><topic>Exact sciences and technology</topic><topic>Logistics</topic><topic>Mathematical programming</topic><topic>Meat industry</topic><topic>Operating costs</topic><topic>Operational research and scientific management</topic><topic>Operational research. 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| ispartof | European journal of operational research, 2009-12, Vol.199 (2), p.409-419 |
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| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Applied sciences Approximation Decision theory. Utility theory Dual decomposition Exact sciences and technology Logistics Mathematical programming Meat industry Operating costs Operational research and scientific management Operational research. Management science Operations research Sample average approximation Stochastic models Stochastic programming Studies Supply chain design Supply chain design Stochastic programming Sample average approximation Dual decomposition Supply chains Uncertainty |
| title | Supply chain design under uncertainty using sample average approximation and dual decomposition |
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