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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/119080...
Part of book or chapter of book . 2006 . Peer-reviewed
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DBLP
Conference object . 2021
Data sources: DBLP
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Rough-Set Multiple-Criteria ABC Analysis

Authors: Ye Chen 0001; Kevin W. Li; Jason K. Levy; Keith W. Hipel; D. Marc Kilgour;

Rough-Set Multiple-Criteria ABC Analysis

Abstract

Multiple-criteria ABC (MCABC) analysis is conducted using a dominance-based rough set approach. ABC analysis, a well-known technique for inventory planning and control, divides stock-keeping units (SKUs) into three classes according to annual dollar usage. But MCABC analysis offers more managerial flexibility by including other criteria, such as lead time and criticality, in the classification of SKUs. The objective of this paper is to propose an MCABC method that uses the dominance-based rough set approach to generate linguistic rules that represent a decision-maker’s preferences based on the classification of a test data set. These linguistic rules are then used to classify all SKUs. A case study demonstrates that the procedure is feasible.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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