UTHM Institutional Repository

Hybrid uncertainties modeling for production planning problems

Mohd. Rahman, Hamijah and Arbaiy, Nureize and Lin, Pei-Chun (2017) Hybrid uncertainties modeling for production planning problems. Communications in Mathematics and Applications, 8 (2). pp. 191-206. ISSN 09758607

Full text not available from this repository.


The formulated mathematical model needs pre-determined and precise model parameters to find a solution. However, the model parameters such as coefficient value are usually not precisely known. Coefficient plays a pivotal role since the coefficient could provide important information in relationship between algebraic and linguistic expression. Existing method which is commonly used to generate the precise parametric values is unable to handle the coexistence of fuzzy information. Moreover, selecting real numbers for coefficients in random process increases the complexity in programming process. Hence, we proposed a fuzzy random regression method in this paper to estimate the precise coefficient values which contains fuzzy random information. An illustrative numerical example is provided to deduce coefficient values from different data representation which included the fuzziness and randomness.The coefficients were treated based on the property of fuzzy random regression. The approach results show that we have the significant capabilities to estimate the coefficient value and improve the model which retain the simultaneous uncertainties and set up in production planning problem.

Item Type: Article
Uncontrolled Keywords: Production planning; coefficient estimation; hybrid uncertainties; fuzzy random variable; fuzzy random regression
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Mar 2019 07:37
Last Modified: 31 Mar 2019 07:37
URI: http://eprints.uthm.edu.my/id/eprint/10903
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item