Classifying inventory using an artificial neural network approach
Computers and Industrial Engineering
A case-based distance model for multiple criteria ABC analysis
Computers and Operations Research
Expert Systems with Applications: An International Journal
Controlling inventory by combining ABC analysis and fuzzy classification
Computers and Industrial Engineering
ABC inventory classification with multiple-criteria using weighted linear optimization
Computers and Operations Research
A neutral DEA model for cross-efficiency evaluation and its extension
Expert Systems with Applications: An International Journal
Multiple criteria inventory classification based on principal components analysis and neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Management of multicriteria inventory classification
Mathematical and Computer Modelling: An International Journal
ABC inventory classification in the presence of both quantitative and qualitative criteria
Computers and Industrial Engineering
A note on hyper ellipse method for classifying biological and medical data
Computers in Biology and Medicine
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Inventory classification is an effective way to manage a large number of items. As a basic methodology, ABC analysis is widely used for classification. The traditional ABC classification is based on only a single criterion. However, it is generally recognized that multiple criteria should be considered in practice. A peer-estimation approach is proposed in this paper for multi-criteria inventory classification (MCIC). The proposed approach determines two common sets of criteria weights and aggregates the resulting two performance scores in the most favorable and least favorable senses for each item without any subjectivity. Comparisons of the proposed approach with some previous methods are illustrated based on a classical MCIC problem. It is shown that our proposed approach can provide a more reasonable and comprehensive performance index for MCIC.