AN INTEGRATED FUZZY MULTI-CRITERIA DECISION-MAKING METHODS FOR SERVICE SELECTION: A SYSTEMATIC LITERATURE REVIEW AND META-ANALYSIS

Abstract

Background: Service selection refers to the process of picking services that best fulfill the user's functional and non-functional requirements. It is possible to pick a web service or a cloud service. Researchers examined a large number of service selection assessments that utilized different services based on Quality of Service (QoS) variables utilizing Multi-Criteria Decision Making Methods. Despite its positive outcomes, earlier research has shown that Multi-Criteria Decision Making Methods alone cannot handle the incompleteness, ambiguity, uncertainty, and, most importantly the fuzziness inherent in decision-making processes due to human involvement. To circumvent these constraints, the usage of Fuzzy Multi-Criteria Decision Making Methods is a growing study topic. Objective: While the research community carefully examined the methodologies used by researchers when studying service selection, there is still a noticeable limited knowledge on how Fuzzy Multi-Criteria Decision Making Methods have been adopted for service selection and whether there are points of improvement to allow for a better service selection evaluation. The purpose of this paper is to offer an overview of and examine the use of Fuzzy Multi-Criteria Decision Making Methods in the subject of service selection with a focus on five research questions. Method: A Systematic Literature Review (SLR) on Fuzzy Multi-Criteria Decision Making Methods for service selection is presented in this work. Our research looks at publications published between 2010 and 2021. Our initial database search resulted in 508 publications. Also, a search through another source (i.e. Reference Lists examination) resulted in 15 publications. After a thorough paper selection process using the PRISMA standard, only 60 publications met the final inclusion criteria. We looked at them from five distinct angles: (i) Quality of Service (QoS) factors used, (ii) Service Application Domains, (iii) Fuzzy Multi-Criteria Decision Making Methods employed, (iv) Dataset used, and (v) Sensitivity Analysis Results: According to the results of the research, Response Time, Success Ability, Reliability, Throughput, and Performance have all been carefully studied in the literature. Other choices, the Cloud service option, and Web service selection received 68 percent, 20%, and 12%, respectively. Ten percent of the research employed heterogeneous datasets, whereas the other ninety percent used homogeneous datasets. The most popular integrated Fuzzy Multi-Criteria Decision Making Method used was the Fuzzy AHP+ Fuzzy TOPSIS. Thirty percent of the research performed a sensitivity analysis, whereas seventy percent did not. The findings indicate that more primary studies combining fuzzy MCDM methods are needed. Also, further reviews can consider the types of fuzzy numbers used as well as the membership functions used. Conclusion: Based on our findings, we believe that Fuzzy Multi-Criteria Decision Making Methods for Service Selection still have room for improvement. This study sets the pace for more primary studies utilizing Fuzzy MCDM methods in the subject of service selection.This, by extension will result in the development of intelligent applications to help service users moving forward. Researchers interested in developing more powerful approaches can look at the findings and conceptualize papers that will combine some powerful fuzzy MCDM techniques based on our overview findings. Also, the Type-3 Fuzzy Logic system can be explored with MCDM Method in service selection research moving forward as it has improved capabilities in terms of handling uncertainties than the others. � 2022 Little Lion Scientific.