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Hibridisasi Algoritma Genetika Dengan Variable Neighborhood Search (VNS) Pada Optimasi Biaya Distribusi

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 4 No. 2 (2017) · pp. 87–96

DOI
10.25126/jtiik.201742287
ISSN
2528-6579, 2355-7699
Published
2017-05-07
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
en

Abstract

AbstrakProses distribusi dianggap sangat penting bagi perusahaan karena menjadi salah satu faktor yang mempengaruhi perolehan keuntungan. Besarnya biaya yang dikeluarkan serta kompleksnya permasalahan dalam proses distribusi menjadikan permasalahan distribusi sebagai topik yang perlu diteliti lebih mendalam lagi. Karena algoritma genetika (AG) sudah terbukti mampu memberikan solusi terbaik pada berbagai macam permasalahan optimasi dan kombinatorial, maka algoritma ini digunakan untuk menyelesaikan permasalahan distribusi pada penelitian ini. Namun, penerapan GA klasik memiliki kekurangan yaitu belum mencapai titik optimum global sehingga perlu dihibridisasi menggunakan algoritma variable neighborhood search (VNS). Algoritma ini dipilih karena selain mencari solusi secara global, algoritma ini juga mencari solusi secara lokal sehingga mampu menutupi kekurangan dari GA. Dengan menggunakan hibridisasi GA dengan VNS maka biaya yang diperoleh adalah 32392960 yang dibuktikan dengan penghematan biaya sebesar 323190 jika dibandingkan dengan GA klasik yaitu 32716150. Namun, dilihat dari waktu komputasi, GA-VNS membutuhkan waktu yang relatif sama dengan GA klasik yaitu 279332 ms (milisecond) dan 265091 ms.Kata kunci: distribusi, algoritma genetika, variable neighborhood searchAbstractThe distribution process is considered importantly for the company as one of the factors that affects profitability. The costs incurred as well as the complexity of the distribution problems makes the distribution problems as a topic that need to be examined more deeply. Since the wide range of combinatorial and optimization problems have been ever solved by using genetic algorithm (GA) well then it is used to resolve the distribution problems in this study. However, the implementation of classical GA has the disadvantage that has not yet reached the global optimum so that needs to be hybridized by using variable neighborhood search (VNS) algorithm. The VNS algorithm has been chosen because its ability either to search the global solutions or local solutions. The local search of VNS algorithm is able to cover the shortage of the GA. By using hibridization of GA with VNS, the cost accrued is 32392960 as evidenced by cost savings of 323190 in comparison with the classical GA is 32716150. However, the computational time of GA-VNS is equal to its classical GA relatively.Keywords: distribution, genetic algorithm, variable neighborhood search

Collaboration network of the authors

40 authors · 67 co-authorship links · 18 across different organisations · showing the 40 most connected

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

  • University of Brawijaya
  • National Research and Innovation Agency
  • Institut Teknologi Kalimantan
  • Indonesian Institute of Sciences
  • State University of Malang
  • Universitas Merdeka Malang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yusuf Priyo AnggodoUniversity of Brawijaya3
Adyan Nur AlfiyatinUniversity of Brawijaya2
Vivi Nur WijayaningrumUniversity of Brawijaya2
Kuncahyo Setyo NugrohoUniversity of Brawijaya2
Safrizal Ardana ArdiyansaUniversity of Brawijaya2
Eric JuliantoUniversity of Brawijaya2
Ervin YohannesUniversity of Brawijaya2
Muchammad Chandra Cahyo UtomoInstitut Teknologi Kalimantan2
Firnanda Al Islama Achyunda PutraUniversity of Brawijaya1
Fitri UtaminingrumUniversity of Brawijaya1
Ida WahyuniUniversity of Brawijaya1
Hilman Nuril HadiUniversity of Brawijaya1
Rizal Setya PerdanaUniversity of Brawijaya1
Ghenniy RachmansyahUniversity of Brawijaya1
Ria FebriyanaUniversity of Brawijaya1
Vitara Nindya Putri HasanUniversity of Brawijaya1
Mohammad Zoqi SarwaniUniversity of Brawijaya1
Luthfi HidayatUniversity of Brawijaya1
Rifki Setya ArmandaUniversity of Brawijaya1
Garsinia Ely RianiUniversity of Brawijaya1
Fita Lathifatul Mu’asyarohAffiliation unknown1
Agus Wahyu WidodoUniversity of Brawijaya1
Diva KurnianingtyasUniversity of Brawijaya1
Candra Fajri AnandaUniversity of Brawijaya1
Farhanna Mar’iUniversity of Brawijaya1
Cleoputri YusainyUniversity of Brawijaya1
Ahmad Afif SupiantoUniversity of Brawijaya1
Vicky ZilvanIndonesian Institute of Sciences1
Hilman Ferdinandus PardedeNational Research and Innovation Agency1
Onky PrasetyoUniversity of Brawijaya1
R. Budiarianto Suryo KusumoNational Research and Innovation Agency1
Qoirul KotimahUniversity of Brawijaya1
Gayatri Dwi SantikaUniversity of Brawijaya1
Agus NabaUniversity of Brawijaya1
Aji Prasetya WibawaState University of Malang1
Nadia Roosmalita SariUniversity of Brawijaya1
Elta SonalithaUniversitas Merdeka Malang1

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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Hibridisasi Algoritma Genetika Dengan Variable Neighborhood Search (VNS) Pada Optimasi Biaya Distribusi</dc:title>
	<dc:creator xml:lang="en">Rahmi, Asyrofa</dc:creator>
	<dc:creator xml:lang="en">Mahmudy, Wayan Firdaus</dc:creator>
	<dc:creator xml:lang="en">Anam, Syaiful</dc:creator>
	<dc:description xml:lang="id">AbstrakProses distribusi dianggap sangat penting bagi perusahaan karena menjadi salah satu faktor yang mempengaruhi perolehan keuntungan. Besarnya biaya yang dikeluarkan serta kompleksnya permasalahan dalam proses distribusi menjadikan permasalahan distribusi sebagai topik yang perlu diteliti lebih mendalam lagi. Karena algoritma genetika (AG) sudah terbukti mampu memberikan solusi terbaik pada berbagai macam permasalahan optimasi dan kombinatorial, maka algoritma ini digunakan untuk menyelesaikan permasalahan distribusi pada penelitian ini. Namun, penerapan GA klasik memiliki kekurangan yaitu belum mencapai titik optimum global sehingga perlu dihibridisasi menggunakan algoritma variable neighborhood search (VNS). Algoritma ini dipilih karena selain mencari solusi secara global, algoritma ini juga mencari solusi secara lokal sehingga mampu menutupi kekurangan dari GA. Dengan menggunakan hibridisasi GA dengan VNS maka biaya yang diperoleh adalah 32392960 yang dibuktikan dengan penghematan biaya sebesar 323190 jika dibandingkan dengan GA klasik yaitu 32716150. Namun, dilihat dari waktu komputasi, GA-VNS membutuhkan waktu yang relatif sama dengan GA klasik yaitu 279332 ms (milisecond) dan 265091 ms.Kata kunci: distribusi, algoritma genetika, variable neighborhood searchAbstractThe distribution process is considered importantly for the company as one of the factors that affects profitability. The costs incurred as well as the complexity of the distribution problems makes the distribution problems as a topic that need to be examined more deeply. Since the wide range of combinatorial and optimization problems have been ever solved by using genetic algorithm (GA) well then it is used to resolve the distribution problems in this study. However, the implementation of classical GA has the disadvantage that has not yet reached the global optimum so that needs to be hybridized by using variable neighborhood search (VNS) algorithm. The VNS algorithm has been chosen because its ability either to search the global solutions or local solutions. The local search of VNS algorithm is able to cover the shortage of the GA. By using hibridization of GA with VNS, the cost accrued is 32392960 as evidenced by cost savings of 323190 in comparison with the classical GA is 32716150. However, the computational time of GA-VNS is equal to its classical GA relatively.Keywords: distribution, genetic algorithm, variable neighborhood search</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2017-05-07</dc:date>
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	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/287</dc:identifier>
	<dc:identifier>10.25126/jtiik.201742287</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 4 No 2: Juni 2017; 87-96</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 4 No. 2: Juni 2017; 87-96</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201742</dc:source>
	<dc:language>en</dc:language>
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	<dc:rights xml:lang="en">Copyright (c) 2017 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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