An enhanced approach for two-sided matching with 2-tuple linguistic multi-attribute preference

  • Yang Lin
  • Ying-Ming WangEmail author
  • Kwai-Sang Chin
Methodologies and Application


This paper focuses on multi-attribute two-sided matching with 2-tuple preferences. A new framework consisting of paring process and feedback process is proposed to generate matching results (couples) in multiple stages. According to the assumption that matching couples should be satisfied with each other, the concept of expected matching ordinal (EMO) is defined and used for filtering unqualified couples in each stage. To derive optimal results, payoff matrices and preference ordinals are firstly obtained on the basis of the preferences given by two-sided players. The paring process formulates a bi-objective optimization model to generate primary matching couples based on the payoff matrices. Subsequently, the feedback process identifies targeted couples from them with the EMO constraint. This mechanism is performed to ensure that matching couples are all mutually satisfied. The novelty of our approach is that we manage matching decisions by balancing individual benefit and party’s benefit. Finally, a practical example is given to illustrate the proposed approach and comparison analysis shows the advantages of our approach. Some related issues are further discussed.


Two-sided matching 2-Tuple linguistic Preference ordinal Feedback mechanism Optimization 



The authors are very grateful to editor in chief and anonymous referees for their insightful and constructive comments and suggestions that have led to an improved version of this paper. The work was partly supported by the National Natural Science Foundation of China (61773123), the Humanities and Social Sciences Foundation of Ministry of Education of China (16YJC630008).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Decision Sciences InstituteFuzhou UniversityFuzhouChina
  2. 2.Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of EducationFuzhou UniversityFuzhouChina
  3. 3.School of EconomicsFujian Normal UniversityFuzhouChina
  4. 4.Department of Systems Engineering and Engineering ManagementCity University of Hong KongKowloon TongHong Kong

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