Intuitionistic Fuzzy Shapley-TOPSIS Method for Multi-Criteria Decision Making Problems Based on Information Measures

Aims & Background: Cloud Computing (CC) offers unique scalable and all time available services to the user. As service provision is an important part of the Cloud computing concept, the proper choice of the desired service is of most relevance according to the users’ needs. Various associations like as Microsoft and Facebook have revealed momentous investments in CC and currently offer services with top levels of reliability. The well-organized and precise evaluation of cloud-based communication network is an essential step in assurance both the business constancy and the continuous open services. However, with a vast diversity in the cloud services, selection of a suitable cloud service is a very challenging task for a user under an unpredictable environment. Due to the multidimensional criteria of Quality of Service (QoS), cloud service selection problems are treated as Multiple Criteria Decision-Making (MCDM) problem. Objectives & Methodology: In present paper, a Multi-Criteria Decision Making (MCDM) method named as Shapley-TOPSIS method, which is extension of classical TOPSIS method for cloud service selection is developed. Thereafter, new divergence measures for IFSs with multiple parameters are studied. The interesting properties among the developed divergence and entropy measures have also been derived. Then, the criterion weights are ascertained by Shapley function via entropy and fuzzy measure approach. Next, Shapleydivergence measures are applied to calculate closeness coefficient of alternative. Finally, a problem of cloud service selection is demonstrated to show the applicability of new method and also compared with some existing method. Results & Conclusion: A decision making problem of cloud computing service provider has been considered for signifying the developed proposed intuitionistic fuzzy Shapley TOPSIS method and finishes with the outcomes coincide with the already developed methods which confirms the solidity of the developed method. For future, we plan to apply the proposed approach with different multi-criteria decision making problems. Also, we can extend proposed approach with other uncertain environment by using other decision making methods.