### Abstract

The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguous. These uncertainties should be included while translating real-world problem into mathematical programming model though handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem hard. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multiobjective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights it's effectiveness.

Original language | English |
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Title of host publication | Proceedings of International Conference on Computational Intelligence, Modelling and Simulation |

Pages | 99-104 |

Number of pages | 6 |

DOIs | |

Publication status | Published - 2012 |

Event | 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012 - Kuantan, Malaysia Duration: 2012 Sep 25 → 2012 Sep 27 |

### Other

Other | 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012 |
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Country | Malaysia |

City | Kuantan |

Period | 12/9/25 → 12/9/27 |

### Fingerprint

### Keywords

- Fractional programming
- Fuzzy random data
- Possibilistic programming
- Vagueness and ambiguity

### ASJC Scopus subject areas

- Computational Theory and Mathematics
- Applied Mathematics
- Modelling and Simulation

### Cite this

*Proceedings of International Conference on Computational Intelligence, Modelling and Simulation*(pp. 99-104). [6338053] https://doi.org/10.1109/CIMSim.2012.42

**Linear fractional programming for fuzzy random based possibilistic programming problem.** / Arbaiy, Nureize Binti; Watada, Junzo.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of International Conference on Computational Intelligence, Modelling and Simulation.*, 6338053, pp. 99-104, 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012, Kuantan, Malaysia, 12/9/25. https://doi.org/10.1109/CIMSim.2012.42

}

TY - GEN

T1 - Linear fractional programming for fuzzy random based possibilistic programming problem

AU - Arbaiy, Nureize Binti

AU - Watada, Junzo

PY - 2012

Y1 - 2012

N2 - The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguous. These uncertainties should be included while translating real-world problem into mathematical programming model though handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem hard. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multiobjective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights it's effectiveness.

AB - The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguous. These uncertainties should be included while translating real-world problem into mathematical programming model though handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem hard. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multiobjective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights it's effectiveness.

KW - Fractional programming

KW - Fuzzy random data

KW - Possibilistic programming

KW - Vagueness and ambiguity

UR - http://www.scopus.com/inward/record.url?scp=84872584706&partnerID=8YFLogxK

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U2 - 10.1109/CIMSim.2012.42

DO - 10.1109/CIMSim.2012.42

M3 - Conference contribution

AN - SCOPUS:84872584706

SP - 99

EP - 104

BT - Proceedings of International Conference on Computational Intelligence, Modelling and Simulation

ER -