Fuzzy knowledge-based approach to treating uncertainty in inventory control

Petrov́ić, Dobrila; Sweeney, Edward

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

Cited by 24 publications

(10 citation statements)

References 8 publications

(9 reference statements)

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“…In order to enable reasoning with fuzzy inputs such as C_F and E_F, the Mamdani-style inference mechanism is modified in the following way [15]. Instead of the fuzzification step, where the input is a crisp value, compatibility between a fuzzy input and the appropriate fuzzy sets low, medium and high are calculated.…”

Section: Inference Mechanismmentioning

confidence: 99%

Fuzzy decision support system for demand forecasting with a learning mechanism

Petrov́ić

Xie

Burnham

2006

Fuzzy Sets and Systems

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Section: Inference Mechanismmentioning

confidence: 99%

Fuzzy decision support system for demand forecasting with a learning mechanism

Petrov́ić

Xie

Burnham

2006

Fuzzy Sets and Systems

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“…Besides, in the literature, there are several researchers presented various types of fuzzy inventory models. For example, Petrovic and Sweeney [12] fuzzified the demand, lead time and inventory level into triangular fuzzy numbers in an inventory control model. Vujosevic et al [16] extended the EOQ model by introducing the fuzziness of ordering cost and holding cost.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand

Chang

Yao

Ouyang

2006

European Journal of Operational Research

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“…as fuzzy [8][9][10][11]. Moreover, researchers presented some inventory models introducing fuzziness in the lead-time demand and annual average demand [12,13].…”

Section: Introductionmentioning

confidence: 99%

On an EOQ model with service level constraint under fuzzy-stochastic demand and variable lead-time

Rong

Maiti

2015

Applied Mathematical Modelling

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a b s t r a c tThis study deals with an EOQ inventory model with fuzzy stochastic demand and controllable lead-time by relaxing the assumption that demand during lead-time follows a specific probability distribution. Here considering the unsatisfied demands to be partially backordered, both lead-time and order quantity are considered as the decision variables. Instead of having a stockout term in the objective function, a service level constraint, which implies that the stockout level per cycle is bounded, is added to the model. This study, we provide an elegant methodology to determine the optimal order quantity and reorder point such that total expected annual cost in fuzzy sense has a minimum value. Finally the proposed model is illustrated by a numerical example.

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Fuzzy knowledge-based approach to treating uncertainty in inventory control

Cited by 24 publications

References 8 publications

Fuzzy decision support system for demand forecasting with a learning mechanism

Fuzzy decision support system for demand forecasting with a learning mechanism

Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand

On an EOQ model with service level constraint under fuzzy-stochastic demand and variable lead-time

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