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Fuzzy Random Based Mean Variance Model for Agricultural Production Planning

  • Mohammad Haris Haikal Othman
  • Nureize ArbaiyEmail author
  • Muhammad Shukri Che Lah
  • Pei-Chun Lin
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 978)

Abstract

Observation and measurement data are the basis of an analysis which usually contains uncertainties. The uncertainties in data need to be properly described as they may increase error in the prediction model. The collected data which contains uncertainty should be adequately treated before analysis. In the portfolio selection problem, uncertainty involves are characterized as fuzzy and random. Hence fuzzy random variables are accounted as input values in the portfolio selection analysis. It is important to preprocess the data sufficiently due to the uncertainties issue. However, only a few studies discuss the systematic procedure for data processing whereby the uncertainties exist. Hence, this study introduces a structure for fuzzy random data processing which deals with fuzziness and randomness in data for building a portfolio selection model. The fuzzy number is utilized to treat the fuzziness and the probability distribution used to treat randomness. The proposed model is applied for agricultural planning. Five types of industrial plants are assessed using the proposed method. The result of this study demonstrates that the proposed method of fuzzy random based data Pre-processing can treat the uncertainties. The systematic procedure of fuzzy random data Pre-processing in this study is important to enable data uncertainties treatment and to reduce error in the early stage of problem model building.

Keywords

Fuzzy random variable Fuzzy random data Data Pre-processing Mean-Variance 
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Notes

Acknowledgments

The author would like to extend its appreciation to the Ministry of Higher Education (MOHE) and Universiti Tun Hussein Onn Malaysia (UTHM). This research is supported by Fundamental Research Grant Scheme (FRGS) Vote No FRGS/1/2019/ICT02/UTHM/02/7 and Geran Penyelidikan Pascasiswazah (GPPS) grant (Vote H332 & Vote U975). The author thanks the anonymous reviewers for the feedback.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mohammad Haris Haikal Othman
    • 1
  • Nureize Arbaiy
    • 1
    Email author
  • Muhammad Shukri Che Lah
    • 1
  • Pei-Chun Lin
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia
  2. 2.Department of Information Engineering and Computer ScienceFeng Chia UniversityTaichungTaiwan

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