Computationally efficient optimization of stock pooling and allocation levels for two-demand-classes under general lead time distributions

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dc.citation.epage974en_US
dc.citation.issueNumber10en_US
dc.citation.spage955en_US
dc.citation.volumeNumber48en_US
dc.contributor.authorVicil, Oğuzhanen_US
dc.contributor.authorJackson, Peteren_US
dc.date.accessioned2018-04-12T10:46:16Z
dc.date.available2018-04-12T10:46:16Z
dc.date.issued2016en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractIn this article we develop a procedure for estimating service levels (fill rates) and for optimizing stock and threshold levels in a two-demand-class model managed based on a lot-for-lot replenishment policy and a static threshold allocation policy. We assume that the priority demand classes exhibit mutually independent, stationary, Poisson demand processes and non-zero order lead times that are independent and identically distributed. A key feature of the optimization routine is that it requires computation of the stationary distribution only once. There are two approaches extant in the literature for estimating the stationary distribution of the stock level process: a so-called single-cycle approach and an embedded Markov chain approach. Both approaches rely on constant lead times. We propose a third approach based on a Continuous-Time Markov Chain (CTMC) approach, solving it exactly for the case of exponentially distributed lead times. We prove that if the independence assumption of the embedded Markov chain approach is true, then the CTMC approach is exact for general lead time distributions as well. We evaluate all three approaches for a spectrum of lead time distributions and conclude that, although the independence assumption does not hold, both the CTMC and embedded Markov chain approaches perform well, dominating the single-cycle approach. The advantages of the CTMC approach are that it is several orders of magnitude less computationally complex than the embedded Markov chain approach and it can be extended in a straightforward fashion to three demand classes.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:46:16Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1080/0740817X.2016.1146421en_US
dc.identifier.issn0740817X
dc.identifier.urihttp://hdl.handle.net/11693/36625
dc.language.isoEnglishen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1080/0740817X.2016.1146421en_US
dc.source.titleIIE Transactions (Institute of Industrial Engineers)en_US
dc.subjectThreshold rationing policiesen_US
dc.subjectMarkov processen_US
dc.subjectInventory controlen_US
dc.subjectPriority demand classesen_US
dc.subjectBackorder systemsen_US
dc.subjectService levelsen_US
dc.subjectService parts logisticsen_US
dc.subjectInventory allocationen_US
dc.titleComputationally efficient optimization of stock pooling and allocation levels for two-demand-classes under general lead time distributionsen_US
dc.typeArticleen_US

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