Abstract
Generally, the fuzzy set concept could be used to deal with the problems with the qualities of ambiguity as well as vagueness. In the decision making process, the reference comparisons for criteria & options tend to be more appropriate to make use of the linguistic variables rather than crisp values in some instances. Meanwhile, the GMIR technique is utilized for the constrained trouble construction to derive the weights of options & criteria, which accomplishes the extension of fuzzy environment. Here in this paper we will study about some basic terms related to K-preference Graded Integration method. We will discuss the fuzzy inventory models under decision maker’s preference (k-preference), and find the optimal solutions of these models, the optimal crisp order quantity or the optimal fuzzy order quantity.
Highlights
As a great deal of money is occupied to the inventories coupled with the increased carrying expenses of theirs, the pharmaceutical companies can't manage to have some money tied up in extra inventories
One method of changing a fuzzy set into a real number is described by picking median that separates the zone under the participation work into halves
To sum up all that's been claimed far, we derive a number of attributes of the representation of fuzzy amounts by utilizing the Graded Mean Integration Representation (GMIR) method below fuzzy arithmetical activities with extension concept
Summary
As a great deal of money is occupied to the inventories coupled with the increased carrying expenses of theirs, the pharmaceutical companies can't manage to have some money tied up in extra inventories. Buying price is expense of ordering raw materials for pharmaceutical generation purposes These include cost of putting a buy order, expense of check up of received batches, certification expenses, etc.Chen and Hsieh introduced the Graded k preference Integration Representation method of generalized fuzzy number depending on the essential worth of graded k preference h levels of generalized fuzzy selection. The graded mean hlevel value of generalized L-R type fuzzy number A=(c, a, b, d; w)[.sub.LR] is h[[L.sup.-1](h) + [R.sup.-1](h)]/. We determine the representation of a generalized L R sort fuzzy number depending on the integration valuation of graded mean h levels as follow. Let A = (c, a, b, d; w)[.sub.LR] be a generalized L-R type fuzzy number, [L.sup.-1] and [R.sup.-1]. We call P(A) as graded [lambda]-preference integration representation of fuzzy number A. The target of this crisp model is to figure out the ideal creation time t1∗which limits the cost per unit time W
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