Fppsv-NHSS: Fuzzy parameterized possibility single valued neutrosophic hypersoft set to site selection for solid waste management

Abstract

The massive increase in urban population has created a slew of socioeconomic and environmental issues. Among these problems, the most notable is the disposal of solid waste and the search for an effective system for it. Many scholars employing various fuzzy set-like methodologies have considered it a fuzzy multi-criteria or multi-attribute decision-making problem due to the involvement of criteria and anticipated uncertainty. The goal of the current study is to use an innovative methodology to tackle the expected uncertainties present in the solid waste site selection problem (SOWSSP). Such uncertainties may be seen in decision-makers’ choices of parameters (or subparameters) and the degree to which they accept approximations for various options. Therefore, a novel mathematical structure called fuzzy parameterized possibility single valued neutrosophic hypersoft set (Fppsv-NHSS) is characterized first and then integrated with modified Sanchez’s method to resolve the SOWSSP through the proposal of an intelligent algorithm. The steps of the proposed algorithm are explained with a self-explanatory example. The relationship between the suitability of solid waste management systems and sites is discussed, and their rankings are determined with rich descriptions of their feasibility. The preferential aspects of this study are that it is capable of managing uncertainties depicted by the nature of parameters and approximations of alternatives by using the concept of fuzzy parameterization and possibility grades respectively. It employs particular mathematical formulations to determine the fuzzy parameterized degrees and possibility grades that are missing in the existing literature. It facilitates the decision-makers to evaluate the alternatives independently, with the choice being indeterminate. With the help of comparison, the computed results are found to be consistent and reliable due to their preferential features.