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PENDEKATAN ALGORITMA GENETIKA DALAM MENYELESAIKAN PERMASALAHAN FUZZY LINEAR PROGRAMMING

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 5 No. 3 (2011) · pp. 36–44

DOI
10.22146/ijccs.5211
ISSN
2460-7258, 1978-1520
Published
2011-11-19
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Fuzzy linear programming is one of the linear programming developments which able to accommodate uncertainty in the real world. Genetic algorithm approach in solving linear programming problems with fuzzy constraints has been introduced by Lin (2008) by providing a case which consists of two decision variables and three constraint functions. Other linear programming problem arise with the presence of some coefficients which are fuzzy in linear programming problems, such as the coefficient of the objective function, the coefficient of constraint functions, and right-hand side coefficients constraint functions. In this study, the problem studied is to explain the genetic algorithm approach to solve linear programming problems where the objective function coefficients and right-hand sides are fuzzy constraint functions.PT Dakota Furniture study case provides a linear programming formulation with a given objective function coefficients and right-hand side coefficients are fuzzy constraint functions. This study describes the use of genetic algorithm approach to solve the problem of linear programming of PT Dakota to maximize the mean income. The genetic algorithm approach is done by simulate every fuzzy number and each fuzzy numbers by distributing them on certain partition points. Then genetic algorithm is used to evaluate the value for each partition point. As a result, the Final Value represents the coefficient of fuzzy number. Fitness function is done by calculating the value of the objective function of linear programming problems. Empirical results indicated that the genetic algorithm approach can provide a very good solution by giving some limitations on each fuzzy coefficient.Genetic algorithm approach can be extended not only to resolve the case of PT Dakota Furniture, but can also be used to solve other linear programming case with some coefficients in the objective function and constraint functions are fuzzy.Keywords: Genetic Algorithm, Fuzzy Linear Programming, Linear Programming, Two-Phase Simplex Method

Keywords

Collaboration network of the authors

14 authors · 17 co-authorship links · 7 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Multimedia Nusantara University
  • Institut Bisnis dan Teknologi Indonesia
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Udayana University
  • State University of Medan
  • Satya Wacana Christian University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Seng HansunMultimedia Nusantara University1
Elena YustinaUniversitas Gadjah Mada1
Desak Made Dwi Utami PutraInstitut Bisnis dan Teknologi Indonesia1
Sylvia Jane Annatje SumarauwAffiliation unknown1
Shofwatul UyunSunan Kalijaga State Islamic University Yogyakarta1
I Wayan SuprianaUdayana University1
Zulfahmi IndraState University of Medan1
Retantyo WardoyoUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Azhari AzhariUniversitas Gadjah Mada1
Reza PulunganUniversitas Gadjah Mada1
Wiwin SulistyoSatya Wacana Christian University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">PENDEKATAN ALGORITMA GENETIKA  DALAM MENYELESAIKAN PERMASALAHAN  FUZZY LINEAR PROGRAMMING</dc:title>
	<dc:creator>Dewi Lestari, Siska</dc:creator>
	<dc:creator>Subanar, Subanar</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Genetic Algorithm, Fuzzy Linear Programming, Linear Programming, Two-Phase Simplex Method</dc:subject>
	<dc:description xml:lang="en-US">Fuzzy linear programming is one of the linear programming developments which able to accommodate uncertainty in the real world. Genetic algorithm approach in solving linear programming problems with fuzzy constraints has been introduced by Lin (2008) by providing a case which consists of two decision variables and three constraint functions. Other linear programming problem arise with the presence of some coefficients which are fuzzy in linear programming problems, such as the coefficient of the objective function, the coefficient of constraint functions, and right-hand side coefficients constraint functions. In this study, the problem studied is to explain the genetic algorithm approach to solve linear programming problems where the objective function coefficients and right-hand sides are fuzzy constraint functions.PT Dakota Furniture study case provides a linear programming formulation with a given objective function coefficients and right-hand side coefficients are fuzzy constraint functions. This study describes the use of genetic algorithm approach to solve the problem of linear programming of PT Dakota to maximize the mean income. The genetic algorithm approach is done by simulate every fuzzy number and each fuzzy numbers by distributing them on certain partition points. Then genetic algorithm is used to evaluate the value for each partition point. As a result, the Final Value represents the coefficient of fuzzy number.  Fitness function is done by calculating the value of the objective function of linear programming problems. Empirical results indicated that the genetic algorithm approach can provide a very good solution by giving some limitations on each fuzzy coefficient.Genetic algorithm approach can be extended not only to resolve the case of PT Dakota Furniture, but can also be used to solve other linear programming case with some coefficients in the objective function and constraint functions are fuzzy.Keywords : Genetic Algorithm, Fuzzy Linear Programming, Linear Programming, Two-Phase Simplex Method</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">IndoCEISS</dc:contributor>
	<dc:date>2011-11-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/5211</dc:identifier>
	<dc:identifier>10.22146/ijccs.5211</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 5, No 3 (2011): November; 36-44</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/5211/4265</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2011 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>