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An Enhanced Possibilistic Programming Model with Fuzzy Random Confidence-Interval for Multi-objective Problem

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Innovative Computing, Optimization and Its Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 741))

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Abstract

Mathematical models are established to represent real-world problems. Since the real-world faces various types of uncertainties, it makes mathematical model suffers with insufficient uncertainties modeling. The existing models lack of explanation in dealing uncertainties. In this paper, construction of mathematical model for decision making scenario with uncertainties is presented. Primarily, fuzzy random regression is applied to formulate a corresponding mathematical model from real application of a multi-objective problem. Then, a technique in possibilistic theory, known as modality optimization is used to solve the developed model. Consequently, the result shows that a well-defined multi-objective mathematical model is possible to be formulated for decision making problems with the uncertainty. Indeed, such problems with uncertainties can be solved efficiently with the presence of modality optimization.

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Acknowledgements

The authors express her appreciation to the University Tun Hussein Onn Malaysia (UTHM) and GATES IT Solution Sdn. Bhd. under its publication scheme. Also, thanks to the First EAI International Conference on Computer Science and Engineering (COMPSE 2016), NOVEMBER 11–12, 2016, PENANG, MALAYSIA.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Soft Computing and Data Mining Center (SMC), Universiti Tun Hussein Onn, Parit Raja, 86400, Batu Pahat, Johor, Malaysia

    Nureize Arbaiy, Noor Azah Samsudin & Aida Mustapa

  2. Department of Computer & Information Sciences, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak, Malaysia

    Junzo Watada

  3. Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Rd., Seatwen, Taichung, 40724, Taiwan, ROC

    Pei-Chun Lin

Authors
  1. Nureize Arbaiy
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  2. Noor Azah Samsudin
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  3. Aida Mustapa
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  4. Junzo Watada
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  5. Pei-Chun Lin
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Corresponding author

Correspondence to Nureize Arbaiy .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VÅ B-TU Ostrava, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

  2. Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Teronoh, Perak, Malaysia

    Pandian Vasant

  3. Ton Duc Thang University, Ho Chi Minh, Vietnam

    Vo Hoang Duy

  4. Ton Duc Thang University, Ho Chi Minh, Vietnam

    Tran Trong Dao

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Arbaiy, N., Samsudin, N.A., Mustapa, A., Watada, J., Lin, PC. (2018). An Enhanced Possibilistic Programming Model with Fuzzy Random Confidence-Interval for Multi-objective Problem. In: Zelinka, I., Vasant, P., Duy, V., Dao, T. (eds) Innovative Computing, Optimization and Its Applications. Studies in Computational Intelligence, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-319-66984-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-66984-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66983-0

  • Online ISBN: 978-3-319-66984-7

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