Advertisement

A bibliometric analysis of Fuzzy Optimization and Decision Making (2002–2017)

  • Dejian Yu
  • Zeshui XuEmail author
  • Wanru Wang
Article
  • 124 Downloads

Abstract

Fuzzy Optimization and Decision Making (FODM) is one of the influential journals in the research field of computer science and operation research, which was found in 2002. In this study, 370 publications published in FODM during 2002 and 2017 were retrieved from the Scopus database, and bibliometric methods are applied to analyze the structure of the FODM journal. First, general statistical analysis based on the number of publications and citations was implemented to find the annual publishing trends, citation structures, most cited publications, and productive authors/institutions/countries/territories. Second, the co-citation networks of cited authors/sources/references were generated; the nodes, links, and total link strengths based on the visualized networks are used to analyze citation connections. Next, to detect the development of the research topics, the co-occurrence networks of keywords of the different stages were illustrated, and the burst detection of keywords is used to identify the emerging topics. Finally, the future challenges of the FODM journal are discussed according to the process and findings of our study. This study provides a systematic and objective view of the FODM journal, which can be helpful for scholars to understand the development and the research structure of this journal.

Keywords

Bibliometric analysis Citation structure Co-citation Keywords co-occurrence Burst detection 

Notes

Acknowledgements

This work was supported by the Project of Philosophy and Social Science in Zhejiang (No. 16NDJC159YB), the China National Natural Science Foundation (Nos. 71771155, 71571123), the Zhejiang Science and Technology Plan of China (No. 2015C33024), and the Zhejiang Provincial Natural Science Foundation of China (No. LY17G010007).

References

  1. Calma, A., & Davies, M. (2015). Studies in higher education 1976–2013: A retrospective using citation network analysis. Studies in Higher Education, 40(1), 4–21.CrossRefGoogle Scholar
  2. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the Association for Information Science and Technology, 57(3), 359–377.Google Scholar
  3. Chen, C., Dubin, R., & Kim, M. C. (2014). Orphan drugs and rare diseases: A scientometric review (2000–2014). Expert Opinion on Orphan Drugs, 2(7), 709–724.CrossRefGoogle Scholar
  4. Chen, X., & Liu, B. (2010). Existence and uniqueness theorem for uncertain differential equations. Fuzzy Optimization and Decision Making, 9(1), 69–81.MathSciNetCrossRefGoogle Scholar
  5. Chen, C., Song, I. Y., Yuan, X., & Zhang, J. (2008). The thematic and citation landscape of data and knowledge engineering (1985–2007). Data & Knowledge Engineering, 67(2), 234–259.CrossRefGoogle Scholar
  6. Cristino, T. M., Neto, A. F., & Costa, A. F. B. (2018). Energy efficiency in buildings: Analysis of scientific literature and identification of data analysis techniques from a bibliometric study. Scientometrics, 114(3), 1275–1326.CrossRefGoogle Scholar
  7. Eito-Brun, R., & Rodríguez, M. L. (2016). 50 years of space research in Europe: A bibliometric profile of the European Space Agency (ESA). Scientometrics, 109(1), 551–576.CrossRefGoogle Scholar
  8. Garfield, E. (1979). Citation indexing: Its theory and application in science, technology, and humanities. New York: Wiley.Google Scholar
  9. Goyal, N. (2017). A “review” of policy sciences: Bibliometric analysis of authors, references, and topics during 1970–2017. Policy Sciences, 50(4), 527–537.CrossRefGoogle Scholar
  10. He, X., Wu, Y., Yu, D. J., & Merigó, J. M. (2017). Exploring the ordered weighted averaging operator knowledge domain: A bibliometric analysis. International Journal of Intelligent Systems, 32(11), 1151–1166.CrossRefGoogle Scholar
  11. Herrera, F., Alonso, S., Chiclana, F., & Herrera-Viedma, E. (2009). Computing with words in decision making: Foundations, trends and prospects. Fuzzy Optimization and Decision Making, 8(4), 337–364.CrossRefGoogle Scholar
  12. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.CrossRefGoogle Scholar
  13. Kim, M. C., & Chen, C. (2015). A scientometric review of emerging trends and new developments in recommendation systems. Scientometrics, 104(1), 239–263.CrossRefGoogle Scholar
  14. Liao, H. C., & Xu, Z. S. (2013). A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making, 12(4), 373–392.MathSciNetCrossRefGoogle Scholar
  15. Liu, B. (2006). A survey of credibility theory. Fuzzy Optimization and Decision Making, 5(4), 387–408.MathSciNetCrossRefGoogle Scholar
  16. Liu, B. (2012). Why is there a need for uncertainty theory? Journal of Uncertain Systems, 6, 3–10.Google Scholar
  17. Liu, Y. K., & Liu, B. (2003). Fuzzy random variables: A scalar expected value operator. Fuzzy Optimization and Decision Making, 2(2), 143–160.MathSciNetCrossRefGoogle Scholar
  18. Madani, F. (2015). ‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis. Scientometrics, 105(1), 323–335.CrossRefGoogle Scholar
  19. Marzi, G., Dabić, M., Daim, T., & Garces, E. (2017). Product and process innovation in manufacturing firms: A 30-year bibliometric analysis. Scientometrics, 113(2), 673–704.CrossRefGoogle Scholar
  20. Merigó, J. M., Pedrycz, W., Weber, R., & De la Sotta, C. (2018). Fifty years of information sciences: A bibliometric overview. Information Sciences, 432, 245–268.MathSciNetCrossRefGoogle Scholar
  21. Merigó, J. M., & Yang, J. B. (2017). A bibliometric analysis of operations research and management science. Omega, 73, 37–48.CrossRefGoogle Scholar
  22. Palomo, J., Figueroa-Domecq, C., & Laguna, P. (2017). Women, peace and security state-of-art: A bibliometric analysis in social sciences based on SCOPUS database. Scientometrics, 113(1), 123–148.CrossRefGoogle Scholar
  23. Peng, Y., Lin, A., Wang, K., Liu, F., Zeng, F., & Yang, L. (2015). Global trends in DEM-related research from 1994 to 2013: A bibliometric analysis. Scientometrics, 105(1), 347–366.CrossRefGoogle Scholar
  24. Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349.Google Scholar
  25. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the Association for Information Science and Technology, 24(4), 265–269.Google Scholar
  26. Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, A computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.CrossRefGoogle Scholar
  27. Xu, Z. S. (2007). Some similarity measures of intuitionistic fuzzy sets and their applications to multiple attribute decision making. Fuzzy Optimization and Decision Making, 6(2), 109–121.MathSciNetCrossRefGoogle Scholar
  28. Xu, Z. S., & Yager, R. R. (2009). Intuitionistic and interval-valued intuitionistic fuzzy preference relations and their measures of similarity for the evaluation of agreement within a group. Fuzzy Optimization and Decision Making, 8(2), 123–139.MathSciNetCrossRefGoogle Scholar
  29. Yager, R. R. (2004). Generalized OWA aggregation operators. Fuzzy Optimization and Decision Making, 3(1), 93–107.MathSciNetCrossRefGoogle Scholar
  30. Yan, E., Ding, Y., & Sugimoto, C. R. (2011). P-Rank: An indicator measuring prestige in heterogeneous scholarly networks. Journal of the Association for Information Science and Technology, 62(3), 467–477.Google Scholar
  31. Yu, D. J., & Shi, S. S. (2015). Researching the development of Atanassov intuitionistic fuzzy set: Using a citation network analysis. Applied Soft Computing, 32, 189–198.CrossRefGoogle Scholar
  32. Yu, D. J., Xu, Z. S., Kao, Y., & Lin, C. T. (2018a). The structure and citation landscape of IEEE transactions on fuzzy systems (1994–2015). IEEE Transactions on Fuzzy Systems, 26(2), 430–442.CrossRefGoogle Scholar
  33. Yu, D. J., Xu, Z. S., Pedrycz, W., & Wang, W. R. (2017). Information sciences 1968–2016: A retrospective analysis with text mining and bibliometric. Information Sciences, 418, 619–634.CrossRefGoogle Scholar
  34. Yu, D. J., Xu, Z. S., & Wang, W. R. (2018b). Bibliometric analysis of fuzzy theory research in China: A 30-year perspective. Knowledge-Based Systems, 141, 188–199.CrossRefGoogle Scholar
  35. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.MathSciNetCrossRefGoogle Scholar
  36. Zhang, Y., Chen, H., Lu, J., & Zhang, G. (2017). Detecting and predicting the topic change of knowledge-based systems: A topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Systems, 133, 255–268.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business SchoolNanjing Audit UniversityNanjingChina
  2. 2.Business SchoolSichuan UniversityChengduChina
  3. 3.School of Information ManagementNanjing UniversityNanjingChina

Personalised recommendations