Quadripartitioned Neutrosophic Pythagorean Soft Set for Financial Cost Estimation in E-Commerce Supply Chain Management

N. Metawa^{1, 2,*}, Sait Revda Dinibutun³, Maha saad Metawea⁴

¹University of Sharjah, UAE

²Tashkent State University of Economics, Uzbekistan

³College of Business Administration, American University of the Middle East, Kuwait

⁴College of business administration, Delta University for Science and Technology, Egypt

Emails: metawa@sharjah.ac.ae; sait.revda@aum.edu.kw; Dr.Mahasaad@hotmail.com

Abstract

The idea of neutrosophic set (NS) from a philosophical viewpoint is a generality of the theory of indeterminacy FS (IFS) and fuzzy set (FS). A NS is considered by a falsity, a truth and indeterminacy membership functions and all membership amount is an actual standard or a non-standard sub-set of the non-standard unit interval ]−0, 1+[. E-commerce is successful for the growth of novel business methods and should be constantly improved in the numerous decades. According to the growing E-commerce, supply chain management (SCM) has been strongly affected as we are now previously overcome by achievement in either developed or developing economies. Nowadays, E-commerce in advanced economy characterizes the newest lead of possibility in physical distribution systems and SCM, even if it emerging economy, e-commerce market is even in its infancy however, it is increasing and become integral part of commercial life. This paper presents a Quadripartitioned Neutrosophic Pythagorean Soft Set-Based Prediction Model for Supply Chain Management (QNPSSPM-SCM) model Using Hybrid Optimization Algorithms. The proposed QNPSSPM-SCM technique is for presenting an advanced E-commerce in SCM using advanced optimization techniques. At first, the min-max normalization method has been applied in the data pre-processing stage to convert input data into a beneficial pattern. In addition, the presented QNPSSPM-SCM system executes quadripartitioned neutrosophic Pythagorean soft set (QNPSS) technique for the prediction process. At last, the hybrid grey wolf optimization and teaching-learning-based optimization (GWO‐TLBO) algorithm fine-tunes the hyperparameter values of the QNPSS model optimally and results in better performance of prediction. The experimental validation of the QNPSSPM-SCM method is verified on a benchmark database and the outcomes are determined regarding different measures. The experimental outcome underlined the development of the QNPSSPM-SCM method in prediction process.

Keywords: Neutrosophic Set; Quadripartitioned Neutrosophic Pythagorean Soft Set; Fuzzy Set (FS); E-commerce; Financial Cost;  Supply Chain Management; Hybrid Optimization Algorithms