Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207541003792243
DC FieldValue
dc.titlePerformance evaluation for tandem multi-factory supply chains: An approximate solution
dc.contributor.authorRuifeng, C.
dc.contributor.authorSubramaniam, V.
dc.date.accessioned2014-06-17T06:30:36Z
dc.date.available2014-06-17T06:30:36Z
dc.date.issued2011-06-01
dc.identifier.citationRuifeng, C., Subramaniam, V. (2011-06-01). Performance evaluation for tandem multi-factory supply chains: An approximate solution. International Journal of Production Research 49 (11) : 3285-3305. ScholarBank@NUS Repository. https://doi.org/10.1080/00207541003792243
dc.identifier.issn00207543
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/61064
dc.description.abstractPredicting the performance of multistage manufacturing systems is usually viewed as challenging because of unexpected machine breakdowns, random processing times, uncertain inter-factory transportation times, etc. In this paper, the authors formulate an approximate model for the tandem manufacturing systems, where the inventory in each buffer is monitored based on the (s, Q) discipline. This model divides a multistage system into a series of primitive line segments, each of which is characterised by a continuous time discrete state Markov process. The model may be applied in two types of systems: (1) tandem flow lines with batch processing and (2) multi-factory manufacturing supply chain, where inter-factory material transportation is required. Based on the model, a number of commonly used performance measures, including throughput, inventory, transportation frequency, etc., can be estimated. These estimates may enable manufacturers to evaluate the performance of the systems, and hence improve the management of production and inventory. © 2011 Taylor & Francis.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1080/00207541003792243
dc.sourceScopus
dc.subjectflow lines
dc.subjectinventory control
dc.subjectMarkov modelling
dc.subjectstochastic models
dc.subjectsupply chain dynamics
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1080/00207541003792243
dc.description.sourcetitleInternational Journal of Production Research
dc.description.volume49
dc.description.issue11
dc.description.page3285-3305
dc.description.codenIJPRB
dc.identifier.isiut000288369500012
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