Fuzzy knowledge-based approach to treating uncertainty in inventory control

Dobrila Petrovic; Edward Sweeney

doi:10.1016/0951-5240(94)90033-7

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Dobrila Petrovic, Edward Sweeney

Research output: Contribution to journal › Article › peer-review

30 Citations (Scopus)

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	https://doi.org/10.1016/0951-5240(94)90033-7
Publication status	Published - Aug 1994

Keywords

inventory control
fuzzy set
knowledge-based system
approximate reasoning

Access to Document

10.1016/0951-5240(94)90033-7

Cite this

@article{ece2dbd5c5bc49fbb106cc47ecf3555c,

title = "Fuzzy knowledge-based approach to treating uncertainty in inventory control",

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.",

keywords = "inventory control, fuzzy set, knowledge-based system, approximate reasoning",

author = "Dobrila Petrovic and Edward Sweeney",

note = "NOTICE: this is the author{\textquoteright}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)90033-7",

year = "1994",

month = aug,

doi = "10.1016/0951-5240(94)90033-7",

language = "English",

volume = "7",

pages = "147--152",

journal = "Computer Integrated Manufacturing Systems",

issn = "0951-5240",

publisher = "Pergamon Press Ltd.",

number = "3",

}

TY - JOUR

T1 - Fuzzy knowledge-based approach to treating uncertainty in inventory control

AU - Petrovic, Dobrila

AU - Sweeney, Edward

N1 - 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)90033-7

PY - 1994/8

Y1 - 1994/8

N2 - 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.

AB - 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.

KW - inventory control

KW - fuzzy set

KW - knowledge-based system

KW - approximate reasoning

U2 - 10.1016/0951-5240(94)90033-7

DO - 10.1016/0951-5240(94)90033-7

M3 - Article

SN - 0951-5240

VL - 7

SP - 147

EP - 152

JO - Computer Integrated Manufacturing Systems

JF - Computer Integrated Manufacturing Systems

IS - 3

ER -

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Abstract

Keywords

Access to Document

Fingerprint

Cite this