Simulation on Real World Problem Using an Integrated MCDM - AWMAC Method for Ranking Alternatives in Fuzzy Optimization

Abstract

The objective of real-world problems comes with two major complexities, namely one with the fixed set of data with high-dimensions and the other with uncertain parameters of high- dimension data set. Fuzzy Interval Value plays a significant role in designing the problem with unsure decision parameters. The constraints which are with more uncertainty within a particular limit can be expressed in terms of Fuzzy Interval Values. The most appropriate method of expressing imprecise data is Fuzzy Interval Values comparing that of Destined Values.  Optimization using FIV sets requires an exact technique which can hold and solve the inexact data set. Interval arithmetic with fuzzy logic is one of the techniques that encompass the parameters and constraints of the problem. FIV optimization is widely applied in designing, marketing and finance, project lining and choice making with a proper definition from the choice makers. Here our grail is to give the optimal solution for the objectives. That is to cluster the number of the alternatives which leads into the minimization of time constraint of the analyzed data in the ranking process. The parameters of the defined data set are having more uncertainty inside the boundary values fixed by the laboratory. For rule depletion and parametric modulation fuzzy average interval valued membership function will be the best option to get an optimized closeness of alternatives. This paper proposed an integrated method of Additive Weightage of Maximized Alternate Closeness. AWMAC in Fuzzy Interval Valued sets and the order preference is compared with the results in EDAS method.