Enhanced Decision Support System Applications Based on Complex Intuitionistic Fuzzy Soft Expert Sets Theory

Malek Alksasbeh,Mohammad Al-Kaseasbeh,Saba Al-Kaseasbeh,Khalid Alemerien,Bassam A Alqaraleh

doi:10.3844/jcssp.2018.601.612

Abstract

Decision making is one of the important milestones in the development of various real-life aspects. Decision Support System (DSS) is a computerized information system used to enhance decision-making in several areas. This study aims to improve DSS applications by extending two concepts of complex intuitionistic fuzzy sets and soft expert sets to propose the theory of complex intuitionistic fuzzy soft expert sets and defines some related concepts pertaining to this notion as well as the basic operations on this concept. Moreover, the algorithm for such concept is developed.

Highlights

The development of computer information systems and the related issues of automating human reasoning and inference have raised a challenge to researchers
Uncertainty is among the most real-life problems which seriously require new concepts, models and systems (Karacapilidis, 2006)
Young et al (2004) improved the theory of soft sets by introduced Intuitionistic Fuzzy Soft Set (IFSS) theory which combines the advantages of soft set and Intuitionistic Fuzzy Set (IFS)

Summary

Introduction

The development of computer information systems and the related issues of automating human reasoning and inference have raised a challenge to researchers. Two decades later, Atanassov (1986) introduced Intuitionistic Fuzzy Set (IFS) as an extension of Zadeh’s notion of the fuzzy set to deal with vagueness and uncertainty as a controlling tool. They are wellknown mathematical tools for supporting the solution of DSSs (Jemal et al, 2017; Zimmermann, 1998). A large number of real-life problems are complicated and cannot be modeled using one-dimensional classes and/or one-dimensional variables To deal with such problems, Ramot et al (2002) introduced Complex Fuzzy Sets (CFS) which characterized by membership function assign with the complex-valued grade.

Literature Reviews

6: Compute ηi

Conclusion