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A greyness reduction framework for prediction of grey heterogeneous data

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Abstract

Existing operational rules of interval grey numbers do not make full use of possible background information when determining the interval boundaries, and this may result in inconsistent results if applying different logical operations. This paper finds that multiplication and division rules of interval grey numbers do not meet the calculation rule of inverse operators. Direct solution and inverse solution of the same interval grey number object may differ not only in numerical ranges but also in greyness degrees. To improve the accuracy of grey number calculation, new operational rules for multiplication and division of interval grey numbers are proposed. Then the traditional prediction modelling method of grey heterogeneous data is refined and expanded by integrating a greyness reduction preprocessing, which is based on the proposed calculation rules. Application of the expanded heterogeneous interval grey number prediction model to a stock replenishment scheduling problem in emergency rescue scenarios is included to illustrate the new operational rules of grey numbers and their application in prediction algorithm, and the proposed approach is compared with other existing methods to demonstrate its effectiveness.

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Acknowledgements

This study was supported by the Leverhulme Trust International Network project entitled “Grey Systems and Its Applications” (Grant Number IN-2014-020), the Natural Science Foundation of China (Grant Number 71401039), the China Scholarship Council Grant Number 2016 06655020), the Humanities and Social Sciences Youth Fund Project of Education Ministry of China (Grant Number 14YJC630060), and the Fujian Natural Science Foundation (Grant Number 2017J01517).

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Authors and Affiliations

  1. School of Economics and Management, Fuzhou University, 2 Xue Yuan Rd., University Town, Fuzhou, 350108, China

    Chong Li

  2. Centre for Computational Intelligence, De Montfort University, Leicester, LE1 9BH, UK

    Chong Li & Yingjie Yang

  3. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Sifeng Liu

Authors
  1. Chong Li
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  2. Yingjie Yang
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  3. Sifeng Liu
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Correspondence to Chong Li.

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Communicated by V. Loia.

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Li, C., Yang, Y. & Liu, S. A greyness reduction framework for prediction of grey heterogeneous data. Soft Comput 24, 17913–17929 (2020). https://doi.org/10.1007/s00500-020-05040-1

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