Abstract
Inventory control in complex manufacturing environments encounters various sources of uncertainity and imprecision. This paper presents one fuzzy knowledge-based approach to solving the problem of order quantity determination, in the presence of uncertain demand, lead time and actual inventory level. Uncertain data are represented by fuzzy numbers, and vaguely defined relations between them are modeled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large numbers of examples. The results of three representative cases are summarized. Finally a comparison between the developed fuzzy knowledge-based and traditional, probabilistic approaches is discussed.
| Original language | English |
|---|---|
| Pages (from-to) | 147-152 |
| Number of pages | 6 |
| Journal | Computer Integrated Manufacturing Systems |
| Volume | 7 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Aug 1994 |
Bibliographical note
NOTICE: this is the author’s version of a work that was accepted for publication in Computer integrated manufacturing systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Petrovic, D & Sweeney, E 1994, 'Fuzzy knowledge-based approach to treating uncertainty in inventory control' Computer integrated manufacturing systems, vol 7, no. 3 (1994) http://dx.doi.org/10.1016/0951-5240(94)90033-7Keywords
- inventory control
- fuzzy set
- knowledge-based system
- approximate reasoning