Assessment of Solid Waste Management Strategies Using an Efficient Complex Fuzzy Hypersoft Set Algorithm Based on Entropy and Similarity Measures
Solid waste management has gained a reputation among environmentalists as it poses a significant threat to the environment when done incorrectly and leading to effects longing for more than a century. Current solid waste management (SWM) concerns are inextricably linked to maintaining mandated organ...
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/2def73fd9a70416aa87c5e71bc6b434e |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Solid waste management has gained a reputation among environmentalists as it poses a significant threat to the environment when done incorrectly and leading to effects longing for more than a century. Current solid waste management (SWM) concerns are inextricably linked to maintaining mandated organic waste treatment and reusing objectives following European directive regulations. Characterizing and spreading uncertainty, as well as verifying forecasts, are all challenges in decision-making. This study presents a multi-attribute decision-making approach based on entropy and similarity measures to evaluate SWM strategies. This research examined the novelty of the complex fuzzy HyperSoft set (CFHSS), which may respond to instabilities, ambiguity, and vagueness of facts in knowledge by simultaneously putting into consideration the amplitude and phase characteristics (P-terms) of complex numbers (C-numbers). The presented structure is the most suitable option for exploring SWM concerns as it allows for a more comprehensive array of membership values, and the periodic nature of the content can be expressed in P-terms to widen the content to a unit circle in a dynamic reference frame through the specification of the fuzzy HyperSoft set (FHSS). Secondly, the features in CFHSS may be further sub-divided into attribute values for easier comprehension. The paper also illustrates the apparent connection between CFHSS similarity measures (SM) and entropy (ENT) and explores colloquial meaning. These strategies may be used to determine the best approach from a group of possibilities that have a variety of applications in the field of optimization. The recommended methodology’s reliability and effectiveness are examined by evaluating the acquired findings to those of several prior studies. An assessment is done using various parameter values to validate the robustness of the suggested approach. |
|---|