### 抄録

The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguity. These uncertainties exist in the problem description and in the preference information in the mathematical programming model. Handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem is difficult to solve. 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 multi-objective 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 its effectiveness.

元の言語 | English |
---|---|

ページ（範囲） | 26-32 |

ページ数 | 7 |

ジャーナル | International Journal of Simulation: Systems, Science and Technology |

巻 | 15 |

発行部数 | 1 |

出版物ステータス | Published - 2014 |

### Fingerprint

### ASJC Scopus subject areas

- Software
- Modelling and Simulation

### これを引用

*International Journal of Simulation: Systems, Science and Technology*,

*15*(1), 26-32.

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

研究成果: Article

*International Journal of Simulation: Systems, Science and Technology*, 巻. 15, 番号 1, pp. 26-32.

}

TY - JOUR

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

AU - Arbaiy, Nureize

AU - Watada, Junzo

PY - 2014

Y1 - 2014

N2 - The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguity. These uncertainties exist in the problem description and in the preference information in the mathematical programming model. Handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem is difficult to solve. 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 multi-objective 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 its effectiveness.

AB - The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguity. These uncertainties exist in the problem description and in the preference information in the mathematical programming model. Handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem is difficult to solve. 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 multi-objective 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 its effectiveness.

KW - Component

KW - Fractional programming

KW - Fuzzy random data

KW - Possibilistic programming

KW - Vagueness and ambiguity

UR - http://www.scopus.com/inward/record.url?scp=84906098080&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906098080&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84906098080

VL - 15

SP - 26

EP - 32

JO - International Journal of Simulation: Systems, Science and Technology

JF - International Journal of Simulation: Systems, Science and Technology

SN - 1473-804X

IS - 1

ER -