### Abstract

A multi-level decision making problem confronts several issues especially in coordinating decision in hierarchic processes and in compromising conflicting objectives for each decision level. Therefore, its mathematical model plays a pivotal role in solving such problem, and is influencing to the final result. However, it is sometimes difficult to estimate the coefficients of objective functions of the model in real situations specifically when the statistical data contain random and fuzzy information. Thus, decision making scheme should provide an appropriate method to handle the presence of such uncertainties. Hence, this paper proposes a fuzzy random regression method to estimate the coefficients value for the objective functions of multi-level multi-objective model. The algorithm is constructed to obtain a satisfaction solution, which fulfills at least weak Pareto optimality. A numerical example illustrates the proposed solution procedure.

Original language | English |
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Title of host publication | IEEE International Conference on Fuzzy Systems |

Pages | 557-563 |

Number of pages | 7 |

DOIs | |

Publication status | Published - 2011 |

Event | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei Duration: 2011 Jun 27 → 2011 Jun 30 |

### Other

Other | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 |
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City | Taipei |

Period | 11/6/27 → 11/6/30 |

### Fingerprint

### Keywords

- additive fuzzy goal programming
- fuzzy random regression model
- multi-level problem
- multi-objective

### ASJC Scopus subject areas

- Software
- Artificial Intelligence
- Applied Mathematics
- Theoretical Computer Science

### Cite this

*IEEE International Conference on Fuzzy Systems*(pp. 557-563). [6007355] https://doi.org/10.1109/FUZZY.2011.6007355

**Multi-level multi-objective decision problem through fuzzy random regression based objective function.** / Arbaiy, Nureize; Watada, Junzo.

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

*IEEE International Conference on Fuzzy Systems.*, 6007355, pp. 557-563, 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011, Taipei, 11/6/27. https://doi.org/10.1109/FUZZY.2011.6007355

}

TY - GEN

T1 - Multi-level multi-objective decision problem through fuzzy random regression based objective function

AU - Arbaiy, Nureize

AU - Watada, Junzo

PY - 2011

Y1 - 2011

N2 - A multi-level decision making problem confronts several issues especially in coordinating decision in hierarchic processes and in compromising conflicting objectives for each decision level. Therefore, its mathematical model plays a pivotal role in solving such problem, and is influencing to the final result. However, it is sometimes difficult to estimate the coefficients of objective functions of the model in real situations specifically when the statistical data contain random and fuzzy information. Thus, decision making scheme should provide an appropriate method to handle the presence of such uncertainties. Hence, this paper proposes a fuzzy random regression method to estimate the coefficients value for the objective functions of multi-level multi-objective model. The algorithm is constructed to obtain a satisfaction solution, which fulfills at least weak Pareto optimality. A numerical example illustrates the proposed solution procedure.

AB - A multi-level decision making problem confronts several issues especially in coordinating decision in hierarchic processes and in compromising conflicting objectives for each decision level. Therefore, its mathematical model plays a pivotal role in solving such problem, and is influencing to the final result. However, it is sometimes difficult to estimate the coefficients of objective functions of the model in real situations specifically when the statistical data contain random and fuzzy information. Thus, decision making scheme should provide an appropriate method to handle the presence of such uncertainties. Hence, this paper proposes a fuzzy random regression method to estimate the coefficients value for the objective functions of multi-level multi-objective model. The algorithm is constructed to obtain a satisfaction solution, which fulfills at least weak Pareto optimality. A numerical example illustrates the proposed solution procedure.

KW - additive fuzzy goal programming

KW - fuzzy random regression model

KW - multi-level problem

KW - multi-objective

UR - http://www.scopus.com/inward/record.url?scp=80053057792&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053057792&partnerID=8YFLogxK

U2 - 10.1109/FUZZY.2011.6007355

DO - 10.1109/FUZZY.2011.6007355

M3 - Conference contribution

AN - SCOPUS:80053057792

SN - 9781424473175

SP - 557

EP - 563

BT - IEEE International Conference on Fuzzy Systems

ER -