A conjunctive multiple-criteria decision-making approach for cloud service supplier selection of manufacturing enterprise

• Published in: Advances in mechanical engineering Vol. 9; no. 3
• Main Authors: Li, Lianhui, Hang, Jiucheng, Sun, Hongxia, Wang, Li
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.03.2017; Sage Publications Ltd; SAGE Publishing
• Subjects: Analytic hierarchy process; Cloud computing; Component and supplier management; Criteria; Decision making; Electronic government; Fuel consumption; Information services; Internet of Things; Maximum entropy method; Neural networks; Quality of service architectures; Software; Cloud service; multiple-criteria decision-making; expert importance degree; integrated weight; supplier selection; connection distance
• ISSN: 1687-8132; 1687-8140
• DOI: 10.1177/1687814016686264

In this article, a conjunctive multiple-criteria decision-making approach for cloud service supplier selection of manufacturing enterprise is proposed. According to the two perspectives of technology and technology management, the index framework of cloud service supplier selection is constructed from four dimensions: cloud service performance, supplier capability, supplier service level, and supplier service quality. Then, a conjunctive multiple-criteria decision-making approach is built. Neural network is used to realize the expert importance degree determination; fuzzy analytic hierarchy process considering the expert importance degree is used to determine the subjective weight; and CRITIC (CRiteria Importance Through Inter-criteria Correlation) is used to determine the objective weight. The multi-weight contribution equilibration model is built to integrate the subjective and objective weight. Finally, the improved TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) replacing Euclidean distance with connection distance is used to sequence the suppliers by their weighted index-value matrix. An automotive manufacturing enterprise application is given finally. The proposed approach can provide a scientific and efficient solution for cloud service supplier selection of manufacturing enterprise.