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PERBANDINGAN KINERJA ALGORITMA GENETIKA DAN SIMULATED ANNEALING UNTUK MASALAH MULTIPLE OBJECTIVE PADA PENJADWALAN FLOWSHOP

Main Authors: Widyadana, I Gede Agus; Faculty of Industrial Technology, Petra Christian University, Pamungkas, Andree; Alumnus, Faculty of Industrial Technology, Petra Christian University
Format: Article info application/pdf eJournal
Bahasa: eng
Terbitan: Institute of Research and Community Outreach - Petra Christian University , 2004
Subjects:
Genetics Algorithm
Simulated Annealing
flow shop
makespan
total flowtime
Online Access: http://jurnalindustri.petra.ac.id/index.php/ind/article/view/16008
http://jurnalindustri.petra.ac.id/index.php/ind/article/view/16008/16000
ctrlnum article-16008
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">PERBANDINGAN KINERJA ALGORITMA GENETIKA DAN SIMULATED ANNEALING UNTUK MASALAH MULTIPLE OBJECTIVE PADA PENJADWALAN FLOWSHOP</title><creator>Widyadana, I Gede Agus; Faculty of Industrial Technology, Petra Christian University</creator><creator>Pamungkas, Andree; Alumnus, Faculty of Industrial Technology, Petra Christian University</creator><subject lang="en-US">Genetics Algorithm, Simulated Annealing, flow shop, makespan, total flowtime.</subject><description lang="en-US">The research is focused on comparing Genetics algorithm and Simulated Annealing in the term of performa and processing time. The main purpose is to find out performance both of the algorithm to solve minimizing makespan and total flowtime in a particular flowshop system. Performances of the algorithms are found by simulating problems with variation of jobs and machines combination. The result show the Simulated Annealing is much better than the Genetics up to 90%. The Genetics, however, only had score in processing time, but the trend that plotted suggest that in problems with lots of jobs and lots of machines, the Simulated Annealing will run much faster than the Genetics. Abstract in Bahasa Indonesia : Penelitian ini difokuskan pada pembandingan algoritma Genetika dan Simulated Annealing ditinjau dari aspek performa dan waktu proses. Tujuannya adalah untuk melihat kemampuan dua algoritma tersebut untuk menyelesaikan problem-problem penjadwalan flow shop dengan kriteria minimasi makespan dan total flowtime. Kemampuan kedua algoritma tersebut dilihat dengan melakukan simulasi yang dilakukan pada kombinasi-kombinasi job dan mesin yang berbeda-beda. Hasil simulasi menunjukan algoritma Simulated Annealing lebih unggul dari algoritma Genetika hingga 90%, algoritma Genetika hanya unggul pada waktu proses saja, namun dengan tren waktu proses yang terbentuk, diyakini pada problem dengan kombinasi job dan mesin yang banyak, algoritma Simulated Annealing dapat lebih cepat daripada algoritma Genetika. Kata kunci: Algoritma Genetika, Simulated Annealing, flow shop, makespan, total flowtime.</description><publisher lang="en-US">Institute of Research and Community Outreach - Petra Christian University</publisher><date>2004-07-09</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>File:application/pdf</type><identifier>http://jurnalindustri.petra.ac.id/index.php/ind/article/view/16008</identifier><identifier>10.9744/jti.4.1.pp. 26-35</identifier><source lang="en-US">Jurnal Teknik Industri; Vol 4, No 1 (2002): JUNE 2002; pp. 26-35</source><language>eng</language><relation>http://jurnalindustri.petra.ac.id/index.php/ind/article/view/16008/16000</relation><rights>Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).&#xA0; </rights><recordID>article-16008</recordID></dc>
language eng
format Journal:Article
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author Widyadana, I Gede Agus; Faculty of Industrial Technology, Petra Christian University
Pamungkas, Andree; Alumnus, Faculty of Industrial Technology, Petra Christian University
title PERBANDINGAN KINERJA ALGORITMA GENETIKA DAN SIMULATED ANNEALING UNTUK MASALAH MULTIPLE OBJECTIVE PADA PENJADWALAN FLOWSHOP
publisher Institute of Research and Community Outreach - Petra Christian University
publishDate 2004
topic Genetics Algorithm
Simulated Annealing
flow shop
makespan
total flowtime
url http://jurnalindustri.petra.ac.id/index.php/ind/article/view/16008
http://jurnalindustri.petra.ac.id/index.php/ind/article/view/16008/16000
contents The research is focused on comparing Genetics algorithm and Simulated Annealing in the term of performa and processing time. The main purpose is to find out performance both of the algorithm to solve minimizing makespan and total flowtime in a particular flowshop system. Performances of the algorithms are found by simulating problems with variation of jobs and machines combination. The result show the Simulated Annealing is much better than the Genetics up to 90%. The Genetics, however, only had score in processing time, but the trend that plotted suggest that in problems with lots of jobs and lots of machines, the Simulated Annealing will run much faster than the Genetics. Abstract in Bahasa Indonesia : Penelitian ini difokuskan pada pembandingan algoritma Genetika dan Simulated Annealing ditinjau dari aspek performa dan waktu proses. Tujuannya adalah untuk melihat kemampuan dua algoritma tersebut untuk menyelesaikan problem-problem penjadwalan flow shop dengan kriteria minimasi makespan dan total flowtime. Kemampuan kedua algoritma tersebut dilihat dengan melakukan simulasi yang dilakukan pada kombinasi-kombinasi job dan mesin yang berbeda-beda. Hasil simulasi menunjukan algoritma Simulated Annealing lebih unggul dari algoritma Genetika hingga 90%, algoritma Genetika hanya unggul pada waktu proses saja, namun dengan tren waktu proses yang terbentuk, diyakini pada problem dengan kombinasi job dan mesin yang banyak, algoritma Simulated Annealing dapat lebih cepat daripada algoritma Genetika. Kata kunci: Algoritma Genetika, Simulated Annealing, flow shop, makespan, total flowtime.
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