A Novel Possibility Complex Spherical Fuzzy Soft Set Approach for Supplier Selection in Multi-Attribute Decision-Making

Abstract

One aspect of supplier selection that is extremely important to the supply chain’s performance and organizational efficiency is supplier selection early in the process. Implementing intelligent decision-support system in the real life supplier evaluation is, however, difficult, since multiple criteria related to supplier’s quality, delivery time, cost effectiveness, reliability, sustainability, flexibility and technological competence are involved. In order to overcome this, in this article a novel idea named the Possibility Complex Spherical Fuzzy Soft Set (SCSpFSS) is introduced for effective Multi-Attribute Decision-Making (MADM). The structure SCSpFSS is intuitively explained in this article through an illustrative example and the fundamental set- theoretical properties of SCSpFSS, such as De Morgan’s law, are discussed. After that, a SCSpFSS based MADM algorithm is subsequently developed and provided in the form of pseudo code for selecting the best supplier in uncertain and vague environment. A supplier selection case study from a real-world application is done to illustrate the practicality of the model and the proposed algorithm is implemented in Python. Apart from supporting decision making process, the python code in the model can also be used to perform thesensitivity analysis using different statistical tools, thus, verifying the robustness and reliability of the model. Moreover, a comparative study is carried out between the proposed model and the available models of MADM and the upgraded decision-making ability of the model presented in this paper has been well explained. Finally this article presented the synthesis of the main results obtained and indicated the possible directions for further a study of integration between soft computing technologies and intelligent supply chain management systems for decision making.