Uncertainty is certain in the world of *** study revisits an economic production quantity(EPQ)model with shortages for stock-dependent demand of the items with reworking and disposing of the imperfect ones over a rand...
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Uncertainty is certain in the world of *** study revisits an economic production quantity(EPQ)model with shortages for stock-dependent demand of the items with reworking and disposing of the imperfect ones over a random planning horizon under the joint effect of inflation and time value of money,where the expected time length is imprecise in *** of learning effect has been incorporated to reduce the defective *** total expected profit over the random planning horizon is maximized subject to the imprecise space *** possibility,necessity and credibility measures have been introduced to defuzzify the *** simulation-based genetic algorithm is used to make decision for the above EPQ model in different measures of *** model is illustrated through an *** analysis shows the impacts of different parameters on the objective function in the model.
In this present work, a new similarity measure of Gaussian fuzzy numbers has been proposed. The proposed method is based on the exponent distance about mean, standard deviation and height of the Gaussian fuzzy numbers...
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In this paper, an EOQ model for breakable items to a retailer has been studied. Here, demand of the business is stock dependent. The breakability of the items is dependent on stock and holding cost. Here, a credit per...
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Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manu...
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In this paper, we define some results on generalized interval-valued intuitionistic fuzzy sets (GIVIFSs). In fact, all interval-valued intuitionistic fuzzy sets (IVIFSs) are GIVIFSs but all GIVIFSs are not IVIFSs. We ...
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An L(0,1)-labelling of a graph G is an assignment of nonnegative integers to the vertices of G such that the difference between the labels assigned to any two adjacent vertices is at least zero and the difference betw...
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An L(0,1)-labelling of a graph G is an assignment of nonnegative integers to the vertices of G such that the difference between the labels assigned to any two adjacent vertices is at least zero and the difference between the labels assigned to any two vertices which are at distance two is at least one. The span of an L(0,1)-labelling is the maximum label number assigned to any vertex of G. The L(0,1)-labelling number of a graph G, denoted by λ0.1(G) is the least integer k such that G has an L(0,1)-labelling of span k. This labelling has an application to a computer code assignment problem. The task is to assign integer control codes to a network of computer stations with distance restrictions. A cactus graph is a connected graph in which every block is either an edge or a cycle. In this paper, we label the vertices of a cactus graph by L(0,1)-labelling and have shown that, △-1≤λ0.1(G)≤△ for a cactus graph, where △ is the degree of the graph G.
The theoretical concepts of graphs are highly utilized by computer science applications, social sciences, and medical sciences, especially in computer science for applications such as data mining, image segmentation, ...
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The theoretical concepts of graphs are highly utilized by computer science applications, social sciences, and medical sciences, especially in computer science for applications such as data mining, image segmentation, clustering, image capturing, and networking. Fuzzy graphs, bipolar fuzzy graphs and the recently developed m -polar fuzzy graphs are growing research topics because they are generalizations of graphs (crisp). In this paper, three new operations, i.e., direct product, semi-strong product and strong product, are defined on m -polar fuzzy graphs. It is proved that any of the products of m -polar fuzzy graphs are again an m -polar fuzzy graph. Sufficient conditions are established for each to be strong, and it is proved that the strong product of two complete m -polar fuzzy graphs is complete. If any of the products of two m -polar fuzzy graphs G 1 and G 2 are strong, then at least G 1 or G 2 must be strong. Moreover, the density of an m -polar fuzzy graph is defined, the notion of balanced m -polar fuzzy graph is studied, and necessary and sufficient conditions for the preceding products of two m -polar fuzzy balanced graphs to be balanced are established. Finally, the concept of product m -polar fuzzy graph is introduced, and it is shown that every product m -polar fuzzy graph is an m -polar fuzzy graph. Some operations, like union, direct product, and ring sum are defined to construct new product m -polar fuzzy graphs.
In many real world problems, data sometimes comes from n agents ( n ≥ 2), i.e., “multipolar information” exists. This information cannot be well-represented by means of fuzzy graphs or bipolar fuzzy grap...
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In many real world problems, data sometimes comes from n agents ( n ≥ 2), i.e., “multipolar information” exists. This information cannot be well-represented by means of fuzzy graphs or bipolar fuzzy graphs. Therefore, m -polar fuzzy set theory is applied to graphs to describe the relationships among several individuals. In this paper, some operations are defined to formulate these graphs. Some properties of strong m -polar fuzzy graphs, self-complementary m -polar fuzzy graphs and self-complementary strong m -polar fuzzy graphs are discussed.
In this paper, we define three operations on intuitionistic fuzzy graphs, viz. direct product, semi-strong product and strong product. In addition, we investigated many interesting results regarding the operations. Mo...
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In this paper, we define three operations on intuitionistic fuzzy graphs, viz. direct product, semi-strong product and strong product. In addition, we investigated many interesting results regarding the operations. Moreover, it is demonstrated that any of the products of strong intuitionistic fuzzy graphs are strong intuitionistic fuzzy graphs. Finally, we defined product intuitionistic fuzzy graphs and investigated many interesting results.
Theoretical concepts of graphs are highly utilized in applications of computer science especially in research area of computer science such as data mining, image segmentation, clustering, image capturing and networkin...
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Theoretical concepts of graphs are highly utilized in applications of computer science especially in research area of computer science such as data mining, image segmentation, clustering, image capturing and networking. An interval-valued fuzzy graph can be obtained from two given interval-valued fuzzy graphs using Cartesian product and composition. In this paper, we introduced the degree of an edge and total degree of an edge. Likewise, we established the degree of an edge in interval-valued fuzzy graphs formed by these operations in terms of the degree of edges in the given interval-valued fuzzy graphs in some particular cases.
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