Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda

被引:32
作者
Amor, Sarah Ben [1 ]
Belaid, Fateh [2 ]
Benkraiem, Ramzi [3 ]
Ramdani, Boumediene [4 ]
Guesmi, Khaled [5 ]
机构
[1] Univ Ottawa, Telfer Sch Management, Ottawa, ON, Canada
[2] Lille Catholic Univ, Fac Management, Fac Management Econ & Sci, UMR 9221 LEM Lille Econ Management, Lille, France
[3] Audencia Business Sch, AACSB, EQUIS & AMBA, Nantes, France
[4] Qatar Univ, Coll Business & Econ, Ctr Entrepreneurship, Doha, Qatar
[5] Paris Sch Business, Ctr Res Energy & Climate Change CRECC, Paris, France
关键词
Multi-criteria; Classification; Sorting; Clustering; Methods; Modelling; Bibliometrics; CRITERIA DECISION-MAKING; ROUGH SET APPROACH; ADDITIVE VALUE-FUNCTIONS; AUTHOR COCITATION; PREFERENCE DISAGGREGATION; SUITABILITY ANALYSIS; SUPPORT-SYSTEM; MCDM; PROMETHEE; MODEL;
D O I
10.1007/s10479-022-04986-9
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, transparency, and auditability of the decision-making process by integrating the performance of options in different criteria and balancing subjective preferences from different stakeholders. This review aims to map the academic research on multi-criteria sorting, classification and clustering methods, and highlights the key research trends and avenues by conducting a bibliometric analysis. We contribute to the body of knowledge in multi-criteria decision analysis in four ways: (1) identifying the most influential articles on this topic, (2) mapping the research on multi-criteria sorting, classification and clustering methods, (3) visualizing the trends in this field of research through network analysis, and (4) highlighting areas for future research. The results of this study help academics and practitioners to navigate the literature on MCDA methods, provide a map of existing evidence, and recommend promising avenues for future research.
引用
收藏
页码:771 / 793
页数:23
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