Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and Its Application on the Cloud Enterprise
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Dual probabilistic multiplicative linguistic preference relationscomparable degreeConsensusexpanding grey relational analysisMulti-criteria decision making (MCDM)
Xie, W., Xu, Z., Ren, Z., & Viedma, E. H. (2019). Expanding grey relational analysis with the comparable degree for dual probabilistic multiplicative linguistic term sets and its application on the cloud enterprise. IEEE Access. 2019
SponsorshipPostgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX18_0199; Scientific Research Foundation of the Graduate School of Southeast University under Grant YBJJ1832; FEDER Financial Support under Grant TIN2016-75850-R
Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a worth pondering question. To help those traditional businesses that have no experience to dispel the clouds and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses the combination of several linguistic terms and their proportions to reveal decision information by opposite angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs) based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it de nes the comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover, it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering the similarity among the EGRA, TODIM, and VIKOR.