Affiliations: [a] Management College, Ocean University of China, Qingdao, China
| [b]
Machine Intelligence Institute, Iona College, New Rochelle, NY, USA
| [c] Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, Sk-810 05 Bratislava, Slovakia, Palacky Univ Olomouc, Fac Sci, Dept Algebra & Geometry, 17 Listopadu 12, Olomouc, Czech Republic
| [d] Business School, Nanjing Normal University, Nanjing, China
Correspondence:
[*]
Corresponding author. LeSheng Jin, Business School, Nanjing Normal University, Nanjing, China E-mail: E-mail: [email protected].
Abstract: The evaluation for online shopping platform is the basis for further decision and policy taking. The collected individual opinion and evaluation information are often represented by some linguistic/preference vectors. Further aggregating those vector needs to simultaneously consider two contradictory factors: the original weights assigned and the inconsistencies involved which requires some new weights assigned. Around those weights allocation factors, to mitigate the negative effect of inconsistency in the collected information, we propose an integrated evaluation model. The model uses the scatter degree as a main indicator, and extends some weights allocation methods such as regular increasing monotone (RIM) quantifier based weights allocation in a new environment, and applies the three sets expression based paradigm and formulation. The proposed model is able to simultaneously give emphasis on those input data with high consistency and to consider the preferences of decision makers. Some detailed evaluation processes and numerical examples are also provided for practitioners to refer to.
Keywords: Aggregation operators, evaluation for online shopping platform, information fusion, multi-criteria decision making, weights adjustment and allocation
