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
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 978)


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.


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



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.


  1. 1.
    Espinoza WHI Fernandez, A, Torres D (2017) The semantic web as a platform against risk and uncertainty in agriculture. In: Working conference on virtual enterprises, pp 753–760. Springer, ChamGoogle Scholar
  2. 2.
    Paulson ND (2007) Three essays on risk and uncertainty in agricultureGoogle Scholar
  3. 3.
    Righi MB, Borenstein D (2018) A simulation comparison of risk measures for portfolio optimization. Finan Res Lett 24:105–112CrossRefGoogle Scholar
  4. 4.
    Markowitz H (1952) Portfolio selection. J Finan 7(1):77–91Google Scholar
  5. 5.
    Shapiro A, Dentcheva D, Ruszczyński, A (2009) Lectures on stochastic programming: modeling and theory. Society for industrial and applied mathematicsGoogle Scholar
  6. 6.
    Uusipaikka E (2008) Confidence intervals in generalized regression models, 1st edn. Chapman and Hall/CRCGoogle Scholar
  7. 7.
    Chuliá, H, Guillé, M, Uribe JM (2017) Measuring uncertainty in the stock market. Int Rev Econ Finan 48:18–33CrossRefGoogle Scholar
  8. 8.
    Sefair JA, Méndez CY, Babat O, Medaglia AL, Zuluaga LF (2017) Linear solution schemes for mean-semi variance project portfolio selection problems: an application in the oil and gas industry. Omega 68:39–48CrossRefGoogle Scholar
  9. 9.
    Lin PC, Watada J, Wu B (2013) Risk assessment of a portfolio selection model based on a fuzzy statistical test. IEICE Trans Inf Syst 96(3):579–588CrossRefGoogle Scholar
  10. 10.
    Aouni B, Doumpos M, Pérez-Gladish B, Steuer RE (2018) On the increasing importance of multiple criteria decision aid methods for portfolio selection. J Oper Res Soc 69(10):1525–1542CrossRefGoogle Scholar
  11. 11.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar
  12. 12.
    Kwakernaak H (1978) Fuzzy random variables-1. Definition and theorems. Inf Sci 29:1–29. Scholar
  13. 13.
    Puri ML, Ralescu DA, Zadeh L (1993) Fuzzy random variables. In: Readings in fuzzy sets for intelligent systems, pp 265–271. Morgan KaufmannGoogle Scholar
  14. 14.
    Lah MSC, Arbaiy N, Efendi R (2019) Stock market forecasting model based on AR (1) with adjusted triangular fuzzy number using standard deviation approach for ASEAN countries. In: Intelligent and interactive computing. Springer, Singapore, pp 103–114CrossRefGoogle Scholar
  15. 15.
    Rahman HM, Arbaiy N, Wen CC, Efendi R (2019) Autoregressive modeling with error percentage spread based triangular fuzzy number. (2):36–40.
  16. 16.
    Rahman HM, Arbaiy N, Efendi R, Wen CC, Info A (2019) Forecasting ASEAN countries exchange rates using auto regression model based on triangular fuzzy number. J Electric Eng Comput Sci 14(3):1525–1532.

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

Personalised recommendations