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An Interactive Method for Solving Fuzzy Multi-objective Linear Programming Problems
International Journal of Advanced Trends in Computer Science and Engineering • 2022
Publication Information
Authors
M. E. Hady Kassem; N. A. El-kholy; M. H. Eid; M. M. Ibrahim
Keywords
Fuzzy set, interactive method, multi-objective
linear programming.
Journal
International Journal of Advanced Trends in Computer Science and Engineering
Publisher
Not Available
Volume
Vol. 10, No. 4, 2021
Issue
2278-3091
Pages
Not Available
publication.type
Local
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
This paper is concerned with multi-objective linear
programming problems in which the coefficients are
expressed as fuzzy numbers of triangular type. An interactive
method, to enhance the weights in the weighted sum problem,
is introduced. In the scalarized problem, the weights are
determined via the ideal minimum and maximum values of
the objective functions. A numerical example is given to
clarify the presented method.
This paper is concerned with multi-objective linear
programming problems in which the coefficients are
expressed as fuzzy numbers of triangular type. An interactive
method, to enhance the weights in the weighted sum problem,
is introduced. In the scalarized problem, the weights are
determined via the ideal minimum and maximum values of
the objective functions. A numerical example is given to
clarify the presented method.
programming problems in which the coefficients are
expressed as fuzzy numbers of triangular type. An interactive
method, to enhance the weights in the weighted sum problem,
is introduced. In the scalarized problem, the weights are
determined via the ideal minimum and maximum values of
the objective functions. A numerical example is given to
clarify the presented method.
This paper is concerned with multi-objective linear
programming problems in which the coefficients are
expressed as fuzzy numbers of triangular type. An interactive
method, to enhance the weights in the weighted sum problem,
is introduced. In the scalarized problem, the weights are
determined via the ideal minimum and maximum values of
the objective functions. A numerical example is given to
clarify the presented method.