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dc.contributor.authorIqbal, Qamar
dc.contributor.authorMalzahn, Don E.
dc.date.accessioned2017-03-10T15:39:05Z
dc.date.available2017-03-10T15:39:05Z
dc.date.issued2017-02
dc.identifier.citationIqbal, Qamar; Malzahn, Don E. 2017. Evaluating discriminating power of single-criteria and multi-criteria models towards inventory classification. Computers & Industrial Engineering, vol. 104, February 2017:pp 219–223en_US
dc.identifier.issn0360-8352
dc.identifier.otherWOS:000393265200018
dc.identifier.urihttp://dx.doi.org/10.1016/j.cie.2016.12.007
dc.identifier.urihttp://hdl.handle.net/10057/12905
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractSingle-criteria and multi-criteria models both are used with regards to inventory classification. In this paper, we evaluated single-criteria and multi-criteria models in terms of their feasibility in classifying inventory items for a given dataset. We introduced discriminating power test. We used two datasets with lead time as the first criterion. We compared the scores of the models. We also modified ZF model and used descending ranking order criteria constraint to address the infeasibilities. Results show that using criteria in descending order reduces the classification infeasibility. Later, we proposed a probability distribution to find the probability of infeasibility for a given dataset against a number of identical scoring items.en_US
dc.language.isoen_USen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofseriesComputers & Industrial Engineering;v.104
dc.subjectInventory classificationen_US
dc.subjectSingle-criteriaen_US
dc.subjectMulti-criteriaen_US
dc.subjectDiscriminating poweren_US
dc.titleEvaluating discriminating power of single-criteria and multi-criteria models towards inventory classificationen_US
dc.typeArticleen_US
dc.rights.holder© 2016 Elsevier Ltd. All rights reserved.en_US


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