विधियों की तुलना करें

	सरलीकृत न्यूट्रोसोफिक हेसिटेंट फजी वातावरण में अधिकतम विचलन के साथ TOPSIS	टोप्सिस का न्यूट्रोसोफिक विस्तार
क्षेत्र	निर्णयन	निर्णयन
परिवार	MCDM	MCDM
उद्भव वर्ष	2019	2016
प्रवर्तक	Akram, M. Naz, S. Smarandache, F.	Biswas, P., Pramanik, S., Giri, B. C.
प्रकार	Simplified Neutrosophic Hesitant Fuzzy TOPSIS — decision matrix entries are SVNHFEs (each of T, I, F is a finite set of values in [0,1]); weights derived internally via Maximizing Deviation Method	Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)
मौलिक स्रोत	Akram, M., Naz, S., Smarandache, F. (2019). Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment. Symmetry	Biswas, P., Pramanik, S., Giri, B. C. (2016). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications
उपनाम	—	—
संबंधित	2	8
सारांश	SNHF-TOPSIS (TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M. Naz, S. Smarandache, F. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	N-TOPSIS (Neutrosophic extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Biswas, P., Pramanik, S., Giri, B. C. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.