Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3392
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dc.contributor.authorRossi R.-
dc.contributor.authorArmagan Tarim S.-
dc.contributor.authorHnich B.-
dc.contributor.authorPrestwich S.-
dc.date.accessioned2023-06-16T14:58:00Z-
dc.date.available2023-06-16T14:58:00Z-
dc.date.issued2007-
dc.identifier.isbn354072396X-
dc.identifier.isbn9783540723967-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://doi.org/10.1007/978-3-540-72397-4_17-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3392-
dc.description4th International Conference on Integration of Artificial Intelligence, Constraint Programming, and Operations Research Techniques for Combinatorial Optimization Problems, CPAIOR 2007 -- 23 May 2007 through 26 May 2007 -- Brussels -- 70750en_US
dc.description.abstractOne of the most important policies adopted in inventory control is the (R,S) policy (also known as the "replenishment cycle" policy). Under the non-stationary demand assumption the (R,S) policy takes the form (R n,Sn) where Rn denotes the length of the n th replenishment cycle, and Sn the corresponding order-up-to-level. Such a policy provides an effective means of damping planning instability and coping with demand uncertainty. In this paper we develop a CP approach able to compute optimal (Rn,Sn) policy parameters under stochastic demand, ordering, holding and shortage costs. The convexity of the cost-function is exploited during the search to compute bounds. We use the optimal solutions to analyze the quality of the solutions provided by an approximate MIP approach that exploits a piecewise linear approximation for the cost function. © Springer-Verlag Berlin Heidelberg 2007.en_US
dc.description.sponsorshipNational Science Foundation, NSF; National Institute of General Medical Sciences, NIGMS: GM22883en_US
dc.description.sponsorshipAcknowledgements: I am grateful for the excellent technical assistance of M. Cary and J. Abcarian. This work was supported by grants from the National Institute of General Medical Sciences Grant No. GM22883 and the National Science Foundation Grant NO. BMS71-01175-AO2.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCost functionsen_US
dc.subjectCostsen_US
dc.subjectDecision makingen_US
dc.subjectProcess planningen_US
dc.subjectRandom processesen_US
dc.subjectUncertainty analysisen_US
dc.subjectDemand assumptionen_US
dc.subjectDemand uncertaintyen_US
dc.subjectPolicy parametersen_US
dc.subjectReplenishment planningen_US
dc.subjectInventory controlen_US
dc.titleReplenishment planning for stochastic inventory systems with shortage costen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1007/978-3-540-72397-4_17-
dc.identifier.scopus2-s2.0-37149045708en_US
dc.authorscopusid35563636800-
dc.authorscopusid6602458958-
dc.authorscopusid7004234709-
dc.identifier.volume4510 LNCSen_US
dc.identifier.startpage229en_US
dc.identifier.endpage243en_US
dc.identifier.wosWOS:000247841700017en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
dc.identifier.wosqualityN/A-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Object-
item.fulltextWith Fulltext-
item.languageiso639-1en-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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