A composite approach to inducing knowledge for expert systems design
Management Science
Identification of membership functions based on fuzzy observation data
Fuzzy Sets and Systems
Classifying inventory using an artificial neural network approach
Computers and Industrial Engineering
Finding fuzzy classification rules using data mining techniques
Pattern Recognition Letters
Fuzzy discriminant analysis with outlier detection by genetic algorithm
Computers and Operations Research
ABC inventory classification with multiple-criteria using weighted linear optimization
Computers and Operations Research
Fuzzy classifier design using genetic algorithms
Pattern Recognition
Management of multicriteria inventory classification
Mathematical and Computer Modelling: An International Journal
Peer-estimation for multiple criteria ABC inventory classification
Computers and Operations Research
A two-phase case-based distance approach for multiple-group classification problems
Computers and Industrial Engineering
Multiple Criteria Inventory Classification Under Fuzzy Environment
International Journal of Fuzzy System Applications
A note on hyper ellipse method for classifying biological and medical data
Computers in Biology and Medicine
Multi-criteria inventory classification with reference items
Computers and Industrial Engineering
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The objective of inventory management is to make decisions regarding the appropriate level of inventory. In practice, all inventories cannot be controlled with equal attention. The most widespread used inventory system is the ABC classification system, but the limitation of the ABC control system is that only one criterion is considered. The purpose of this paper is to propose a new inventory control approach called ABC-fuzzy classification (ABC-FC), which can handle variables with either nominal or non-nominal attribute, incorporate manager's experience, judgment into inventory classification, and can be implemented easily. Our ABC-FC approach is implemented based on the data of the Keelung Port. The results of our study show that 59 items are identified as very important group, 69 items as important group, and the remaining 64 items as unimportant group. By comparing the results of ABC-FC with the original data, we find that our ABC-FC analysis shows a high accuracy of classification. Some concluding remarks and suggestions for inventory control are also provided.