Abstract
Various types of uncertainties and imprecision are inherent in real inventory problems. They are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by usual probabilistic models. The questions how to define inventory optimization tasks in such environment and how to interpret optimal solutions arise. This paper considers the modification of EOQ formula in the presence of imprecisely estimated parameters. For example, holding and ordering costs are often not precisely known and are usually expressed by linguistic terms such as: "Holding cost is approximately of value ch", or: "Ordering cost is about value co or more". These imprecise parameters are presented by fuzzy numbers, defined on a bounded interval on the axis of real numbers. Alternative approaches to determining the optimal order quantity in a fuzzy environment are developed, illustrated by a selection of examples, and discussed.
| Original language | English |
|---|---|
| Pages (from-to) | 499-504 |
| Number of pages | 6 |
| Journal | International Journal of Production Economics |
| Volume | 45 |
| Issue number | 1-3 |
| DOIs | |
| Publication status | Published - 1 Aug 1996 |
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Keywords
- EOQ
- Fuzzy arithmetic
- Fuzzy number
- Imprecision
- Uncertainty
ASJC Scopus subject areas
- Economics and Econometrics
- Industrial and Manufacturing Engineering
Cite this
EOQ formula when inventory cost is fuzzy. / Vujošević, Mirko; Petrovic, Dobrila; Petrović, Radivoj.
In: International Journal of Production Economics, Vol. 45, No. 1-3, 01.08.1996, p. 499-504.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - EOQ formula when inventory cost is fuzzy
AU - Vujošević, Mirko
AU - Petrovic, Dobrila
AU - Petrović, Radivoj
PY - 1996/8/1
Y1 - 1996/8/1
N2 - Various types of uncertainties and imprecision are inherent in real inventory problems. They are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by usual probabilistic models. The questions how to define inventory optimization tasks in such environment and how to interpret optimal solutions arise. This paper considers the modification of EOQ formula in the presence of imprecisely estimated parameters. For example, holding and ordering costs are often not precisely known and are usually expressed by linguistic terms such as: "Holding cost is approximately of value ch", or: "Ordering cost is about value co or more". These imprecise parameters are presented by fuzzy numbers, defined on a bounded interval on the axis of real numbers. Alternative approaches to determining the optimal order quantity in a fuzzy environment are developed, illustrated by a selection of examples, and discussed.
AB - Various types of uncertainties and imprecision are inherent in real inventory problems. They are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by usual probabilistic models. The questions how to define inventory optimization tasks in such environment and how to interpret optimal solutions arise. This paper considers the modification of EOQ formula in the presence of imprecisely estimated parameters. For example, holding and ordering costs are often not precisely known and are usually expressed by linguistic terms such as: "Holding cost is approximately of value ch", or: "Ordering cost is about value co or more". These imprecise parameters are presented by fuzzy numbers, defined on a bounded interval on the axis of real numbers. Alternative approaches to determining the optimal order quantity in a fuzzy environment are developed, illustrated by a selection of examples, and discussed.
KW - EOQ
KW - Fuzzy arithmetic
KW - Fuzzy number
KW - Imprecision
KW - Uncertainty
U2 - 10.1016/0925-5273(95)00149-2
DO - 10.1016/0925-5273(95)00149-2
M3 - Article
VL - 45
SP - 499
EP - 504
JO - International Journal of Production Economics
JF - International Journal of Production Economics
SN - 0925-5273
IS - 1-3
ER -