Abstract
Inventory control in complex manufacturing environments encounters various sources of uncertainty 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 modelled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large number 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 |
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Keywords
- approximate reasoning
- fuzzy set
- inventory control
- knowledge-based system
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
Cite this
Fuzzy knowledge-based approach to treating uncertainty in inventory control. / Petrovic, Dobrila; Sweeney, Edward.
In: Computer Integrated Manufacturing Systems, Vol. 7, No. 3, 08.1994, p. 147-152.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fuzzy knowledge-based approach to treating uncertainty in inventory control
AU - Petrovic, Dobrila
AU - Sweeney, Edward
PY - 1994/8
Y1 - 1994/8
N2 - Inventory control in complex manufacturing environments encounters various sources of uncertainty 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 modelled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large number 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.
AB - Inventory control in complex manufacturing environments encounters various sources of uncertainty 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 modelled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large number 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.
KW - approximate reasoning
KW - fuzzy set
KW - inventory control
KW - knowledge-based system
U2 - 10.1016/0951-5240(94)90033-7
DO - 10.1016/0951-5240(94)90033-7
M3 - Article
VL - 7
SP - 147
EP - 152
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
SN - 0736-5845
IS - 3
ER -