Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/29855
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSantos, Marcio Costapt_PT
dc.contributor.authorAgra, Agostinhopt_PT
dc.contributor.authorPoss, Michaelpt_PT
dc.date.accessioned2020-11-20T20:07:33Z-
dc.date.issued2020-06-
dc.identifier.issn0254-5330pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/29855-
dc.description.abstractWe consider a robust inventory problem where products are perishable with a given shelf life and demands are assumed uncertain and can take any value in a given polytope. Interestingly, considering uncertain demands leads to part of the production being spoiled, a phenomenon that does not appear in the deterministic context. Based on a deterministic model we propose a robust model where the production decisions are first-stage variables and the inventory levels and the spoiled production are recourse variables that can be adjusted to the demand scenario following a FIFO policy. To handle the non-anticipativity constraints related to the FIFO policy, we propose a non-linear reformulation for the robust problem, which is then linearized using classical techniques. We propose a row-and-column generation algorithm to solve the reformulated model to optimality using a decomposition algorithm. Computational tests show that the decomposition approach can solve a set of instances representing different practical situations within reasonable amount of time. Moreover, the robust solutions obtained ensure low losses of production when the worst-case scenarios are materialized.pt_PT
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationUID/MAT/04106/2019pt_PT
dc.rightsembargoedAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectLot-sizingpt_PT
dc.subjectInteger programmingpt_PT
dc.subjectRobust optimizationpt_PT
dc.subjectRow-and-column generation algorithmspt_PT
dc.titleRobust inventory theory with perishable productspt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage473pt_PT
degois.publication.issue2pt_PT
degois.publication.lastPage494pt_PT
degois.publication.titleAnnals of Operations Researchpt_PT
degois.publication.volume289pt_PT
dc.date.embargo2021-07-01-
dc.relation.publisherversionhttps://link.springer.com/article/10.1007%2Fs10479-019-03264-5pt_PT
dc.identifier.doi10.1007/s10479-019-03264-5pt_PT
dc.identifier.essn1572-9338pt_PT
Appears in Collections:CIDMA - Artigos
DMat - Artigos
OGTCG - Artigos

Files in This Item:
File Description SizeFormat 
AnnalsOperResearch.pdfMain file256.02 kBAdobe PDFembargoedAccess


FacebookTwitterLinkedIn
Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.