Economic reorder point for fuzzy backorder quantity

In traditional continuous review reorder point problems, probability theory has been wildly explored to deal with uncertain cases. In most situations, the decision maker is assumed to be aware of the probability distribution of uncertainty when the probability theory is applied. However, this is seldom the case. In the most situations, the uncertainty is estimated within a certain interval without any knowledge of the probability distribution within the interval. In this investigation, the application of fuzzy sets theory is introduced for continuous review reorder point problems. It is assumed that uncertainties may appear in the demand over the lead time and in holding costs where decisionmaking is characterised by the lack of precise future estimates of the uncertain information. The minimised possible total cost is obtained by a corresponding reorder point and quantity. The computational aspect of the fuzzy model and its interpretations are illustrated by examples. Finally, deterministic approximations to this fuzzy approach are also investigated.

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“…A triangular fuzzy set (2,3,4) is used to express the holding cost. Let the costs per product be precise: i.e.…”

Section: The Fuzzy Continuous Review Reorder Point Modelmentioning

confidence: 99%

“…An optimal ordering quantity is obtained to achieve a maximum profit. Chang et al [2] modify the economic reorder point problem by introducing a fuzzy backorder quantity. The results offer a better way of using economic fuzzy quantities.…”

Section: Introductionmentioning

confidence: 99%

Continuous review reorder point problems in a fuzzy environment

Pai

Hsu

2003

The International Journal of Advanced Manufacturing Technology

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“…Chen and Hsieh [3] established a fuzzy economic production model to treat the inventory problem with all the parameters and variables being fuzzy numbers. Chang et al [2], Lee and Yao [6], Lin and Yao [7], Yao et al [17], used the extension principle and centroid method to find the total cost in the fuzzy sense, and showed that it is close to the crisp total cost. Hsieh [5] has developed fuzzy production inventory model with trapezoidal fuzzy number and found the optimum production quantity and total cost in the fuzzy sense using graded mean integration method.…”

Section: Introductionmentioning

confidence: 99%

Optimal policy for fuzzy expected value production inventory model with imprecise production preparation-time

Soni¹,

Shah

2011

Int. J. Mach. Learn. & Cyber.

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A great deal of research has been done on economic production quantity (EPQ) model, which concern deterministic or stochastic or fuzzy demand and cost situations. In this paper, the EPQ model with imprecise demand and production preparation time is considered which are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Based on an expected value criterion or a credibility criterion, a fuzzy expected value model (EVM) is constructed. The purpose of the fuzzy EVM is to determine the optimal policy such that the fuzzy expected value of the total cost is minimal. In order to obtain the exact expected value directly, instead of relying on simulation procedure, the results for uncertain variables with uncertainty distribution are exploited by treating them as fuzzy variables with credibility distribution. The mathematical analysis is carried out to compute exact expected values. Numerical study is also provided to demonstrate the contribution of our model.

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“…These types of problems are de-fuzzified first using a suitable fuzzy technique and then the solution procedure can be obtained in the usual manner. Several authors, namely Chang et al (1998), Lee and Yao (1998), Lin and Yao (2000), Yao et al (2000), De, Kundu and Goswami (2003), De and Goswami (2006) and Gani and Maheswari (2010) developed inventory models in fuzzy sense by considering different parameters as fuzzy parameters.…”

Section: Introductionmentioning

confidence: 99%

EOQ in fuzzy environment and trade credit

Shah¹,

Pareek²,

Sangal³

2012

ijiec

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Now-a-days, the offer of credit period to the retailer for settling the account for the units purchased by the supplier is considered to be the most beneficial policy. In this article, an attempt is made to formulate an economic order quantity model under fuzzy environment where delay in payment for the retailer is permissible. The demand rate, ordering cost and selling price per item are taken as triangular fuzzy numbers. The α-cut representation method is used to calculate the optimum cycle time and total optimum cost. The optimum cycle time and total optimum cost in fuzzy sense is de-fuzzified using the centre of gravity method.

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Economic reorder point for fuzzy backorder quantity

Cited by 129 publications

References 1 publication

Continuous review reorder point problems in a fuzzy environment

Continuous review reorder point problems in a fuzzy environment

Optimal policy for fuzzy expected value production inventory model with imprecise production preparation-time

EOQ in fuzzy environment and trade credit

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