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Possibility neutrosophic hypersoft set is the generalisation of possibility neutrosophic soft set, and it is the blend of possibility neutrosophic set and hypersoft set. It tackles the insufficiencies of possibility intuitionistic fuzzy set and soft set for the entitlement of degree of indeterminacy and multi-argument approximate function, respectively. This kind of function maps the sub-parametric tuples to the power set of universe. It emphasises the partitioning of each attribute into its attribute-valued set that is missing in the existing fuzzy soft set-like structures. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. This research aims to initiate a novel structure that hybridises the possibility neutrosophic set with the hypersoft set for dealing with uncertainties efficiently. Therefore, the possibility neutrosophic hypersoft set is developed by employing theoretic, axiomatic, graphical and algorithmic approaches. After conceptual characterisation of its essential elementary notions, decision-support systems are presented based on AND and OR operations with the proposal of algorithms to assist the decision-making process. Similarity measures between possibility neutrosophic hypersoft sets are elaborated with the support of daily life applications. The comparison of the proposed work is discussed with the existing relevant models under certain evaluating features. [ABSTRACT FROM AUTHOR]

The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today's business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q-SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q-spherical fuzzy (p, q-SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we expended the concept of neutrosophic sets (NS) by introducing the idea α , β , γ - neutrosophic set ( α , β , γ - NS). The existing models under conventional NSs, fail to adequately address the management of membership degree influence during the aggregation process. While the proposed framework manages the influence of membership degree (MD), indeterminacy membership degree (IMD), and non-membership degree (NMD) by incorporating parameters α , β , and γ . Furthermore, we defined some fundamental operational laws for α , β , γ - NSs and introduced a series of aggregation operators (AOs) to effectively combine α , β , γ - neutrosophic information. Based on these AOs, a new Multiple Criteria Decision Making (MCDM) model is proposed for solving real-life decision-making (DM) challenges. An illustrative case study is presented to showcase the effectiveness of the proposed model in selecting an optimal location for a software office. The article concludes by validating the proposed model's authenticity and effectiveness through a comparative analysis with existing approaches., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)