Integrated Plithogenic Quantum Decision Making on Smart and  Sustainable Electrical Material Selection

Nivetha Martin; Davron Aslonqulovich Juraev; Florentin Smarandache; F. X.  Edwin Deepak; P. Sathya; Gabriel XG Yue

Authors

Nivetha Martin Department of Mathematics Arul Anandar College (Autonomous) Karumathur, Affiliated to Madurai Kamaraj University, India.
Davron Aslonqulovich Juraev Scientific Research Center, Baku Engineering University, Baku AZ0102, Azerbaijan,
Florentin Smarandache Department of Mathematics University of New Mexico Gallup, USA
F. X. Edwin Deepak Dept. of Electrical and Electronics Enigineering, National College of Engineering Kovilpatti, India,
P. Sathya Department of Mathematics, G.T.N.Arts College(Autonomous), Dindigul, Affiliated to Madurai Kamaraj University, India
Gabriel XG Yue Department of Computer Science and Engineering, European University Cyprus, Nicosia, Cyprus,

Keywords:

Electrical materials, Smart, Sustainable, Plithogenic logic, Quantum

Abstract

The evolving modern electrical and energy systems are driven by the dual characteristics of
sustainability and intelligence. The materials are expected to exhibit more efficiency and good
performance with low environmental impacts. This paper develops a novel decision-making model
integrating Plithogenic logic with a quantum decision-making approach. This newly developed model
addresses the constraints of uncertainty and conflicting criteria in the selection of electrical materials. In
addition to the attributes of electrical efficiency, economic viability, thermal stability, environmental
sustainability and smart system integration, this decision-making model also considers the attribute
values to rank the electrical materials classified as conductors, semiconductors, superconductors,
insulators and photovoltaic materials. The newly developed model with Plithogenic accuracy functions
and principles of quantum uncertainty is more effective in ranking electrical materials. Sensitivity analysis
is performed with different degrees of appearance to analyze the ranking results under various scenarios.
This work certainly contributes to the development of a more resilient decision-making approach, paving
the way for smart and sustainable energy infrastructure augmentation.

DOI 10.5281/zenodo.17420158