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ITS » Master Theses » Manajemen Operasional (S2)
Posted by anis at 24/12/2006 13:25:59  •  27680 Views


STUDI PERFORMANSI ALGORITMA GENETIKA DALAM PENCAPAIAN NILAI SOLUSI PADA PENYELESAIAN MULTI OBJECTIVE GOAL PROGRAMMING

Author :
ARIFIN, MIFTAHOL 




ABSTRAK

Pada problem optimasi yang mempunyai multiple objective seringkali tujuannya merupakan konflik dalam pencapaian nilai optimal dari ruang permasalahan dimensi tinggi high dimensional dan seringkali membutuhkan proses perhitungan yang rumit. Cara algoritmis yang umum dipakai dalam menyelesaikan permasalahan multi objective biasanya dengan menggunakan teknik konvensional seperti goal programming compromise programming dan interactive methods. Teknik-teknik ini efektif ketika menghadapi model dengan goal yang tidak terlalu banyak namun tidak efektif ketika menghadapi model yang kurang jelas dan kompleks jumlah variabel. subject to dan goal terlalu besar Algoritma genetika memberikan alternatif baru dalam memecahkan banyak model sulit dari bidang optimalisasi. Metodologi yang digunakan dalam menyelesaikan persoalan multi objective goal programming dengan pendekatan GA terlebih dahulu dilakukan dengan merubah subject to menjadi nilai domain constrain. Nilai range dari domain constrain ini kemudian diacak pada populasi tertentu untuk menentukan nilai tertinggi dari proses pencarian. Hasil akhir menunjukkan bahwa GA mampu menuntun penelusuran titik-titik optimaldalam persoalan multi objective goal programming. Titik-titik tersebut didapatkan dengan lebih efisien dibandingkan dengan proses QS3 atau LINDO. GA mampu memberikan nilai pencapaian solusi terhadap solusi idealnya lebih baik dibanding dengan metode lainnya.Hal ini disebabkan karena pada saat eksekusi GA lebih tergantung pada panjang populasi dan panjang generasi sedangkan pada metode lainnya tergantung pada jumlah variabel yang akan dieksekusi.


ABSTRACT

In problem of optimization having multiple objectives the goals often conflict each in achieving optimum value from the space of high dimensional issues and it frequently needs some complicated calculation process. The commonly used algorithm method for solving the problem of multi objective usually employees a conventional technique such as goal programming compromise programming and interactive methods. Those techniques are effective with less clear and complex model variable number subject to and too large goal. Genetics algorithm gives a new alternative in solving many complicated models in the area of optimization. The methodology adopted in the solving the problem of multi objective goal programming with GA approach was done by changing subject to into domain constrains value. Range value from domain constrain was then randomized in certain population to determine the highest value of the searching process. The final result shows that GA is able to guide in tracing the optimum points in the multi objective programming. The points are obtained more efficiently relative to the process of QS3 or LINDO. GA is able to give the value of solution achievement on its ideal solution better than the other methods do. It is because on the execution GA depends more on the population length and generation length while in the other method GA depends on the variable number to execute.



KeywordsMulti objective goal programming; solusi ideal ; algoritma genetika ; Multi objective goal programming; ideal solution; genetics algorithm
 
Subject:  Algoritma
Contributor
  1. Dr.Ir. Udisubakti C., M.Eng.Sc.
    Ir.Sri Gunani Partiwi, MT.
Date Create: 24/12/2006
Type: Text
Format: pdf ; 99 pages
Language: Indonesian
Identifier: ITS-Master-3100003018397
Collection ID: 3100002014804
Call Number: 511.8 Ari s


Source
Theses Industrial Engineering RT 511.8 Ari s, 2001

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