Posynomial Geometric Programming in EOQ Model with Interval Neutrosophic Number

In this paper, we applied the concept of triangular Neutrosophic number from a special viewpoint. Additionally, we utilized specific varieties of linear triangular Neutrosophic numbers and de-neutrosofication idea which could be very critical for uncertainty concept. Here, an EOQ model has been developed for a linearly dependent demand of non-instantaneous items under shortages. The paper considers holding cost as triangular neutrosophic number (TNN) and optimizes the model. A comparative study is done under crisps and neutrosophic domain and the model gives better result under the later domain. This noble notion will assist us to resolve a plethora of realistic existence problems in neutrosophic area.

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Optimization of EOQ model with space constraint: An intuitionistic fuzzy geometric programming approach

Notes on Intuitionistic Fuzzy Sets ◽

10.7546/nifs.2018.24.4.172-189 ◽

2018 ◽

Vol 24 (4) ◽

pp. 172-189 ◽

Cited By ~ 1

Author(s):

Bappa Mondal ◽

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Arindam Garai ◽

Tapan Kumar Roy ◽

◽

...

Keyword(s):

Space Constraint ◽

Geometric Programming ◽

Programming Approach ◽

Intuitionistic Fuzzy ◽

Eoq Model

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A Stochastic Decision Support System for Economic Order Quantity Problem

Advances in Fuzzy Systems ◽

10.1155/2012/650419 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8

Author(s):

Amir Yousefli ◽

Mehdi Ghazanfari

Keyword(s):

Decision Support ◽

Probability Distribution ◽

Objective Function ◽

Geometric Programming ◽

Distribution Functions ◽

Economic Order Quantity ◽

Order Quantity ◽

Probability Distribution Functions ◽

Eoq Model ◽

Decision Variables

Improving decisions efficiency is one of the major concerns of the decision support systems. Specially in the uncertain environment, decision support systems could be implemented efficiently to simplify decision making process. In this paper stochastic economic order quantity (EOQ) problem is investigated in which decision variables and objective function are uncertain in nature and optimum probability distribution functions of them are calculated through a geometric programming model. Obtained probability distribution functions of the decision variables and the objective function are used as optimum knowledge to design a new probabilistic rule base (PRB) as a decision support system for EOQ model. The developed PRB is a new type of the stochastic rule bases that can be used to infer optimum or near optimum values of the decision variables and the objective function of the EOQ model without solving the geometric programming problem directly. Comparison between the results of the developed PRB and the optimum solutions which is provided in the numerical example illustrates the efficiency of the developed PRB.

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