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International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

IPMU 2018: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications pp 63–73Cite as

DNBMA: A Double Normalization-Based Multi-Aggregation Method

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 855))

Abstract

We propose a double normalization-based multi-aggregation method to deal with the multi-criteria decision making problems considering the benefit, cost and target criterion values. To do so, we introduce an enhanced target-based linear normalization formula and a target-based vector normalization formula. Given that different normalization techniques maintain special advantages and disadvantages, we combine them with three aggregation models to describe the alternatives’ performance from different aspects. Then, a new integration approach is developed to integrate three types of subordinate utility values and ranks to derive the final ranking. The selected alternative not only has a comprehensive performance but does not perform badly under each criterion. Finally, the proposed method is highlighted by a case study of selecting an optimal innovation enterprise.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (71501135, 71771156), the Fundamental Research Funds for the central Universities (No. YJ201535), and the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23).

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610064, China

    Huchang Liao & Xingli Wu

  2. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Huchang Liao & Francisco Herrera

  3. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Francisco Herrera

Authors
  1. Huchang Liao
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  2. Xingli Wu
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  3. Francisco Herrera
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Corresponding author

Correspondence to Xingli Wu .

Editor information

Editors and Affiliations

  1. Universidad de Cádiz, Cádiz, Cadiz, Spain

    Jesús Medina

  2. Universidad de Málaga, Málaga, Málaga, Spain

    Manuel Ojeda-Aciego

  3. Universidad de Granada, Granada, Spain

    José Luis Verdegay

  4. Institute for Research and Applications, University of Ostrava, Ostrava, Czech Republic

    Irina Perfilieva

  5. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  6. Iona College, New Rochelle, New York, USA

    Ronald R. Yager

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Liao, H., Wu, X., Herrera, F. (2018). DNBMA: A Double Normalization-Based Multi-Aggregation Method. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-91479-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91478-7

  • Online ISBN: 978-3-319-91479-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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