Near optimality guarantees for data-driven newsvendor with temporally dependent demand: A Monte Carlo approach

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dc.citation.epage2653en_US
dc.citation.spage2643en_US
dc.contributor.authorAkcay, Alpen_US
dc.contributor.authorBiller, B.en_US
dc.contributor.authorTayur, S.en_US
dc.coverage.spatialWashington, DC, USAen_US
dc.date.accessioned2016-02-08T12:04:55Z
dc.date.available2016-02-08T12:04:55Z
dc.date.issued2013en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.descriptionDate of Conference: 8-11 Dec. 2013en_US
dc.description.abstractWe consider a newsvendor problem with stationary and temporally dependent demand in the absence of complete information about the demand process. The objective is to compute a probabilistic guarantee such that the expected cost of an inventory-target estimate is arbitrarily close to the expected cost of the optimal critical-fractile solution. We do this by sampling dependent uniform random variates matching the underlying dependence structure of the demand process - rather than sampling the actual demand which requires the specification of a marginal distribution function - and by approximating a lower bound on the probability of the so-called near optimality. Our analysis sheds light on the role of temporal dependence in the resulting probabilistic guarantee, which has been only investigated for independent and identically distributed demand in the inventory management literature. © 2013 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:04:55Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2013en
dc.identifier.doi10.1109/WSC.2013.6721636en_US
dc.identifier.urihttp://hdl.handle.net/11693/27914
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/WSC.2013.6721636en_US
dc.source.title2013 Winter Simulations Conference (WSC)en_US
dc.titleNear optimality guarantees for data-driven newsvendor with temporally dependent demand: A Monte Carlo approachen_US
dc.typeConference Paperen_US

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