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primary topic: An enhanced possibilistic programming model with fuzzy random confidence-interval for multi-objective problem

An enhanced possibilistic programming model with fuzzy random confidence-interval for multi-objective problem
type: AcademicArticle
type: Article
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. © Springer International Publishing AG 2018.
date: 2018
volume: 741
is part of: Studies in Computational Intelligence
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Springer Science and Business Media Deutschland GmbH
type: Organization

Studies in Computational Intelligence
type: Collection
issn: 1860949X

J. Watada
type: Person
given name: J.
family name: Watada

N. Arbaiy
type: Person
given name: N.
family name: Arbaiy

A. Mustapa
type: Person
given name: A.
family name: Mustapa

N.A. Samsudin
type: Person
given name: N.A.
family name: Samsudin

P.-C. Lin
type: Person
given name: P.-C.
family name: Lin

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primary topic: An enhanced possibilistic programming model with fuzzy random confidence-interval for multi-objective problem