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Solving an EOQ model under fuzzy reasoning
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DOI：10.1016/j.asoc.2020.106892.png)
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Abstract
En 中文
In general fuzzy system, the nature of a fuzzy number is usually viewed as specific and deterministic but, in fuzzy reasoning the membership function is developed in a randomized sense. Here, we have studied the concept of fuzzy approximate reasoning over the modelling of a cost minimization classical economic order quantity (EOQ) inventory management problem. We have developed the model where all parameters assume randomized fuzzy set by means of fuzzy approximate reasoning. First of all, considering the probability density function of the fuzzy variable, utilizing possibility measures of fuzzy sets within we have formulated the expectations of the fuzzy membership functions then we split the model into seven several sub models accordingly. To defuzzify the fuzzy functions the traditional alpha-cuts and its dual k-cuts have been utilized over several feasible spaces of dual spaces of fuzzy variables. Numerical illustrations under different alpha-dual k cuts of the objective functions are done with the help of LINGO software via solution algorithm. Moreover, a comparative study has been done with the existing general fuzzy solution. Managerial insights are also highlighted by showing the superiority of the proposed approach. Finally, sensitivity analysis and graphical illustrations are made to justify the model. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
EOQ model
Fuzzy set
Fuzzy reasoning
alpha-dual cuts
Duality
Optimization
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