OPTIMASI RUTE ANTAR JEMPUT SISWA DENGAN ALGORITMA GENETIK (Studi Kasus di SD Islam Al-Azhar)

Hendrasmoro, Prananto (2011) OPTIMASI RUTE ANTAR JEMPUT SISWA DENGAN ALGORITMA GENETIK (Studi Kasus di SD Islam Al-Azhar). Undergraduate thesis, Diponegoro University.

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Abstract

ABSTRAK Antar jemput SD Islam Al-Azhar merupakan fasilitas yang disediakan untuk para siswa SD Islam Al-Azhar. Permasalahan antar jemput termasuk vehicle routing problem (VRP). Pada pagi hari para siswa dijemput di rumah masing-masing, dan di siang hari diantar kembali ke rumah masing-masing. Rumah para siswa tersebar di berbagai wilayah kota Semarang. Kebijakan antar jemput dikelola oleh koperasi SD Islam Al-Azhar. Agar antar jemput SD Islam Al-Azhar efisien, maka perlu dicari rute optimum. Algoritma genetik merupakan metode yang dapat diterapkan untuk menyelesaikan masalah optimisasi seperti mencari rute optimum. Semakin banyak generasi dan semakin banyak populasi akan membuat algoritma genetik semakin baik, serta pemberian nilai untuk parameter crossover probability (pc), mutation probability (pm) berpengaruh pada hasil pencarian rute optimum. Hasil yang diperoleh menggunakan algoritma genetik dapat berbeda setiap kali dieksekusi, karena algoritma genetik menggunakan bilangan acak pada prosesnya. Hasil yang diperoleh pada penelitian ini adalah 26 rute dengan 22 kendaraan, yang terbagi dalam 3 shift. ABSTRACT Shuttle SD Islam Al-Azhar is a facility provided for the students of Al-Azhar Islamic elementary school. Shuttle car problem is an vehicle routing problem (VRP). On the morning the students picked up in their homes, and in the afternoon transfer back to their homes. Houses for students are in all regions of the city of Semarang. Pickup Policies managed by koperasi SD Islam Al-Azhar. For shuttle car SD Islam Al-Azhar efficient, it is necessary to find the optimum route. Genetic algorithms are methods that can be applied to solve optimization problems such as finding the optimum route. The more generations and the more the population will make the genetic algorithm is better, and giving a value for the parameter crossover probability (pc), mutation probability (pm) effect on the optimum route search results. Result obtained using genetic algorithms can be different each time executed, because the genetic algorithm uses random numbers in the process. Results obtained in this study are 26 routes with 22 vehicles, on three sift.

Item Type:Thesis (Undergraduate)
Uncontrolled Keywords:Kata Kunci : algoritma genetik, antar jemput, vehicle routing problem Keywords: genetic algorithms, shuttle, vehicle routing problem
Subjects:T Technology > T Technology (General)
T Technology > TS Manufactures
Divisions:Faculty of Engineering > Department of Industrial Engineering
Faculty of Engineering > Department of Industrial Engineering
ID Code:33182
Deposited By:Admin teknik industri
Deposited On:08 Feb 2012 12:09
Last Modified:08 Feb 2012 12:09

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