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dc.contributor.authorKalpana, P
dc.contributor.authorKaur, Arshinder
dc.date.accessioned2017-01-30T14:42:07Z
dc.date.available2017-01-30T14:42:07Z
dc.date.created2014-03-23T20:00:50Z
dc.date.issued2013
dc.identifier.citationKalpana, P. and Kaur, A. 2013. Ordering decisions of single period split order supply chain with various demand distributions. International Journal of Operational Research. 16 (3): pp. 263-286.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/40372
dc.identifier.doi10.1504/IJOR.2013.052332
dc.description.abstract

This paper considers a two-echelon supply chain (SC) trading short life-cycle products whose production/delivery lead-times are longer than the selling season. We investigate the conventional single period inventory model and introduce the concept of splitting a single order into two orderings. The proposed split ordering model is compared with the case of single ordering in two scenarios: (1) decentralised case, where the SC members operate independently and (2) centralised case, where the SC members work together as a single system. The objective of this paper is to maximise the total expected profit and to reduce the risk of carrying inventory of short life-cycle products. We construct a dynamic programming model to determine optimal order quantity for the second period ordering, which considers leftover units and unmet demand in the first period to revise the order quantity in the second period. The proposed split ordering model considers ordering once ahead of the selling season and hence an additional set-up cost is not incurred for the second replenishment. Results that are taken using commercial software Mathematica 7 confirm better results over the current studies. Further, the proposed model is experimented for different demand distribution.

dc.publisherInderscience Publishers
dc.subjectsingle period inventory models
dc.subjectsplit ordering opportunity
dc.subjectdynamic programming
dc.titleOrdering decisions of single period split order supply chain with various demand distributions
dc.typeJournal Article
dcterms.source.volume16
dcterms.source.number3
dcterms.source.startPage263
dcterms.source.endPage286
dcterms.source.issn17457653
dcterms.source.titleInternational Journal of Operational Research
curtin.department
curtin.accessStatusFulltext not available


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