Please use this identifier to cite or link to this item: https://doi.org/10.1287/opre.1040.0140
DC FieldValue
dc.titleStochastic transportation-inventory network design problem
dc.contributor.authorShu, J.
dc.contributor.authorTeo, C.-P.
dc.contributor.authorShen, Z.-J.M.
dc.date.accessioned2013-10-09T03:26:34Z
dc.date.available2013-10-09T03:26:34Z
dc.date.issued2005
dc.identifier.citationShu, J., Teo, C.-P., Shen, Z.-J.M. (2005). Stochastic transportation-inventory network design problem. Operations Research 53 (1) : 48-60. ScholarBank@NUS Repository. https://doi.org/10.1287/opre.1040.0140
dc.identifier.issn0030364X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/44089
dc.description.abstractWe study the stochastic transportation-inventory network design problem involving one supplier and multiple retailers. Each retailer faces some uncertain demand, and safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. Shen et al. (2003) formulated this problem as a set-covering integer-programming model. The pricing problem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization problem. In this paper, we show that by exploiting certain special structures, we can solve the general pricing problem in Shen et al. efficiently. Our approach utilizes the fact that the set of all lines in a two-dimension plane has low VC-dimension. We present computational results on several instances of sizes ranging from 40 to 500 retailers. Our solution technique can be applied to a wide range of other concave cost-minimization problems. © 2005 INFORMS.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1287/opre.1040.0140
dc.sourceScopus
dc.subjectFacilities/equipment planning: stochastic
dc.subjectInventory/production: uncertainty, stochastic
dc.subjectProgramming: nonlinear
dc.typeArticle
dc.contributor.departmentDECISION SCIENCES
dc.description.doi10.1287/opre.1040.0140
dc.description.sourcetitleOperations Research
dc.description.volume53
dc.description.issue1
dc.description.page48-60
dc.description.codenOPREA
dc.identifier.isiut000228443300004
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