Affiliations: [a] Department of Mathematics and Statistics, Hazara University, Mansehra, KP Pakistan
| [b] Future University in Egypt, New Cairo, Egypt
| [c] Department of Mathematics, College of Science and Arts, Qassim University, Al-Badaya, Saudi Arabia
| [d] Department of Operations and Management Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt
| [e] EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Correspondence:
[*]
Corresponding author. Muhammad Rahim, Department of Mathematics and Statistics, Hazara University, Mansehra 21300, KP Pakistan. E-mail: raheem_maths@hu.edu.pk.
Abstract: 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.
