Title
A large group decision-making method and its application to the evaluation of property perceived service quality.
Abstract
With the development of modern property service industry, the property perceived service quality (PPSQ) evaluation data is characterized by multiple evaluation subjects, complicated data structure and large scale data. Since the traditional decision-making methods are difficult to solve the above similar problems, this paper proposes a large group decision-making (LGDM) method of generalized multi-attribute and multi-scale (MAMS) based on the linear programming technique for multidimensional analysis of preference (LINMAP). In this method, the large-scale heterogeneous data of expert preference and user evaluation is fused. The decision matrix of generalized MAMS is used to process user evaluation information. The positive ideal solution (PIS) and the attribute weights are determined by the LINMAP model. The comprehensive evaluation values are calculated and hereby the alternatives are ranked order. According to the relation between attribute weights and preset values, a mechanism for identifying invalid data is designed. This paper analyzes a set of survey data of PPSQ for the four public construction projects in the same city. The analysis results show the validity and rationality of the proposed method, and develop the property service evaluation theory.
Year
DOI
Venue
2019
10.3233/JIFS-182934
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Large group decision-making,generalized multi-attribute and multi-scale method,linear programming technique for multidimensional analysis of preference,large-scale heterogeneous data processing,property perceived service quality
Service quality,Knowledge management,Artificial intelligence,Machine learning,Mathematics,Group decision-making
Journal
Volume
Issue
ISSN
37
1
1064-1246
Citations 
PageRank 
References 
1
0.34
0
Authors
4
Name
Order
Citations
PageRank
Wen-Jin Zuo110.34
Deng-Feng Li296846.12
Gao-Feng Yu320.69
Li-Ping Zhang410.34