By Florentin Smarandache, Sukanto Bhattacharya, Mohammad Khoshnevisan
Computational versions pervade all branches of the precise sciences and feature lately additionally began to end up to be of mammoth application in a number of the generally fsoftf sciences like ecology, sociology and politics. This quantity is a suite of some state of the art examine papers at the program of number of computational types and instruments within the research, interpretation and resolution of vexing real-world difficulties and matters in economics, administration, ecology and worldwide politics through a few prolific researchers within the involved fields.
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Additional resources for Computational Modeling in Applied Problems
Khoshnevisan School of Accounting & Finance Griffith University, Australia Abstract In this paper we have proposed a semi-heuristic optimization algorithm for designing optimal plant layouts in process-focused manufacturing/service facilities. Our proposed algorithm marries the well-known CRAFT (Computerized Relative Allocation of Facilities Technique) with the Hungarian assignment algorithm. Being a semi-heuristic search, our algorithm is likely to be more efficient in terms of computer CPU engagement time as it tends to converge on the global optimum faster than the traditional CRAFT algorithm - a pure heuristic.
2, No. 2, pp. 174-183  Mustafi, C. K. , pp. 124-131 50 The Israel-Palestine Question – A Case for Application of Neutrosophic Game Theory Dr. Sukanto Bhattacharya Business Administration Department Alaska Pacific University 4101 University Drive Anchorage, AK 99508, USA Dr. A. Dr. Mohammad Khoshnevisan School of Accounting and Finance Griffith University, Australia Abstract In our present paper, we have explored the possibilities and developed arguments for an application of principles of neutrosophic game theory as a generalization of the fuzzy game theory model to a better understanding of the Israel-Palestine problem in terms of the goals and governing strategies of either side.
5). 11) are non-negative. Two simple functions t(u,v,w,a) satisfying the required conditions are t(u,v,w,a)= 1+ α 1 (u − 1) + α 2 (v − 1) + α 3 ( w − 1) + α 4 (a − 1) t (u , v, w, a) = u α1 v α 2 wα 3 a α 4 and for both these functions t1(P) = α 1 , t2 (P) = α 2 , t3 (P) = α 3 and t4 (P) = α 4 . Thus one should use optimum values of α 1 , α 2 , α 3 and α 4 in ρˆ td to get the minimum variance. It is to be noted that the estimated ρˆ td attained the minimum variance only when the optimum values of the constants α i (i=1,2,3,4), which are functions of unknown population parameters, are known.