An Enhanced MCDM Model for Cloud Service Provider Selection

Abstract

Multi-Criteria Decision-Making (MCDM) techniques are often used to aid decision-makers in selecting the best alternative among several options. However, these systems have issues, including the Rank Reversal Problem (RRP) and decision-making ambiguity. This study aimed to propose a selection model for a Cloud Service Provider (CSP) that addresses these issues. This research used the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the alternatives. The entropy technique is utilized to determine the weight of the criteria, and Single Valued Neutrosophic (SVN) is employed to address uncertainty. To select the best cloud provider based on Quality of Service (QoS) criteria, we used a dataset from Cloud Harmony for this study. The results indicated that the suggested model could effectively resolve the RRP under conditions of uncertainty. This research is novel and is the first to address both the problem of uncertainty in decision-making and RRP in MCDM.